diff --git a/.Rbuildignore b/.Rbuildignore
index 55c2e741..1b29c38b 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^do-not-share$
diff --git a/.Rprofile b/.Rprofile
new file mode 100644
index 00000000..a4766fdb
--- /dev/null
+++ b/.Rprofile
@@ -0,0 +1,5 @@
+if(interactive()){
+  library(devtools)
+  library(testthat)
+  library(vdiffr)
+}
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 16be22f7..1447f7ec 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -1,115 +1,97 @@
+name: R-CMD-check
+
 on:
   push:
-    branches:
-      - main
-      - master
-      - pre-release
+    branches: [main, master, pre-release]
   pull_request:
-    branches:
-      - main
-      - master
-      - pre-release
-
-name: R-CMD-check
+    branches: [main, master, pre-release]
 
 jobs:
-  R-CMD-check:
+  build:
     runs-on: ${{ matrix.config.os }}
-
-    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
-
     strategy:
       fail-fast: false
       matrix:
         config:
           - {os: windows-latest, r: 'release'}
-          - {os: ubuntu-latest,  r: 'release'}
-        #  - {os: macOS-latest,   r: 'release'}
-        #  - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
-        #  - {os: ubuntu-latest,   r: 'oldrel-1'}
-
-    env:
-      RSPM: ${{ matrix.config.rspm }}
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-      R_REMOTES_NO_ERRORS_FROM_WARNINGS: false
-      NOT_CRAN: true
-
+          - {os: ubuntu-latest, r: 'release'}
+          - {os: macOS-latest, r: 'release'}
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
-      - uses: r-lib/actions/setup-r@v2
+      - name: Setup R
+        uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
 
-      - uses: r-lib/actions/setup-pandoc@v2
-
-      - name: Query dependencies
+      # Install JAGS and system dependencies on Linux
+      - name: Install System Dependencies (Linux)
+        if: runner.os == 'Linux'
         run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-        shell: Rscript {0}
-
-      - name: Cache R packages
-        if: runner.os != 'Windows'
-        uses: actions/cache@v1
-        with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-r-${{ matrix.config.r }}-3-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ runner.os }}-r-${{ matrix.config.r }}-3-
-
-      - name: Set path for RTools 4.0
-        if: runner.os == 'Windows'
-        run: echo "C:/rtools40/usr/bin;C:/rtools40/mingw64/bin" | Out-File -Append -FilePath $env:GITHUB_PATH -Encoding utf8
-      
-      - name: Install most Windows system dependencies
+          sudo apt-get update
+          sudo apt-get install -y \
+            libssl-dev \
+            libcurl4-openssl-dev \
+            libxml2-dev \
+            libfontconfig1-dev \
+            libharfbuzz-dev \
+            libfribidi-dev \
+            libfreetype6-dev \
+            libpng-dev \
+            libtiff5-dev \
+            libjpeg-dev \
+            libcairo2-dev \
+            jags
+
+      # Install JAGS on Windows
+      - name: Install JAGS (Windows)
         if: runner.os == 'Windows'
+        shell: cmd
         run: |
-          pacman -Syu mingw-w64-x86_64-make --noconfirm
-          g++ --version
-          Get-Command g++ | Select-Object -ExpandProperty Definition
-          mingw32-make --version
-          Get-Command mingw32-make | Select-Object -ExpandProperty Definition
-          (New-Object System.Net.WebClient).DownloadFile('https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe', 'C:\JAGS-4.3.1.exe')
-        shell: powershell
+          curl -o jags-installer.exe -L https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe/download
+          jags-installer.exe /S
+          del jags-installer.exe
 
-      - name: Install JAGS on Windows
+      # Set JAGS environment variables on Windows
+      - name: Set JAGS environment variables (Windows)
         if: runner.os == 'Windows'
-        run: C:\JAGS-4.3.1.exe /S
         shell: cmd
-
-      - name: Install Linux system dependencies
-        if: runner.os == 'Linux'
-        env:
-          RHUB_PLATFORM: linux-x86_64-ubuntu-gcc
         run: |
-          sudo apt-get install -y libglpk-dev
-          Rscript -e "remotes::install_github('r-hub/sysreqs')"
-          sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))")
-          sudo -s eval "$sysreqs"
-          sudo -s apt-get install jags
-          sudo -s apt-get install libv8-dev
-          sudo -s apt-get install libnode-dev
-          sudo -s apt-get install libcurl4-openssl-dev
+          echo JAGS_ROOT=C:\Program Files\JAGS\JAGS-4.3.1>> %GITHUB_ENV%
+          echo JAGS_MAJOR_VERSION=4>> %GITHUB_ENV%
+          echo PATH=C:\Program Files\JAGS\JAGS-4.3.1\bin;%PATH%>> %GITHUB_ENV%
 
-      - name: Install Mac system dependencies
+      # Install JAGS on macOS
+      - name: Install JAGS (macOS)
         if: runner.os == 'macOS'
         run: |
-          rm '/usr/local/bin/gfortran'
-          brew install automake jags
-          
-      - name: Install dependencies
-        if: runner.os != 'macOS'
+          brew update
+          brew install jags
+
+      # Find and Set JAGS Paths
+      - name: Set Environment Variables (macOS)
+        if: runner.os == 'macOS'
         run: |
-          remotes::install_deps(dependencies = TRUE, type = ifelse(tolower(Sys.info()["sysname"]) == "linux", "source", "binary"))
-          remotes::install_cran("rcmdcheck")
-        shell: Rscript {0}
+          JAGS_BIN=$(brew --prefix jags)/bin
+          echo "JAGS_ROOT=$(brew --prefix jags)" >> $GITHUB_ENV
+          echo "PATH=$JAGS_BIN:$PATH" >> $GITHUB_ENV
+          echo "PKG_CONFIG_PATH=$(brew --prefix jags)/lib/pkgconfig" >> $GITHUB_ENV
 
-      - name: Install dependencies (macOS)
+      # Create Symlink for jags-terminal (if necessary)
+      - name: Create Symlink for jags-terminal (macOS)
         if: runner.os == 'macOS'
         run: |
-          remotes::install_deps(dependencies = TRUE, type = ifelse(tolower(Sys.info()["sysname"]) == "linux", "source", "binary"))
-          install.packages("rjags", type = "source")
-          remotes::install_cran("rcmdcheck")
+          sudo mkdir -p $(brew --prefix)/libexec
+          sudo ln -s $(brew --prefix jags)/bin/jags $(brew --prefix)/libexec/jags-terminal
+
+      # Install the package
+      - name: Install the required packages
+        run: |
+          options(repos = c(CRAN = "https://cloud.r-project.org"))
+          install.packages('devtools')
+          install.packages('rcmdcheck')
+          install.packages(c('BayesFactor', 'RoBMA', 'runjags', 'rjags', 'rstan', 'scales', 'vdiffr', 'testthat', 'covr', 'pandoc'))
+          devtools::install()
         shell: Rscript {0}
 
       - name: Check
@@ -117,6 +99,7 @@ jobs:
         env:
           _R_CHECK_CRAN_INCOMING_REMOTE_: false
         run: |
+          options(crayon.enabled = TRUE)
           rcmdcheck::rcmdcheck(
             args        = c("--no-manual", "--as-cran", "--ignore-vignettes"),
             build_args  = c("--no-build-vignettes"),
@@ -131,16 +114,17 @@ jobs:
           JAGS_ROOT: "/c/progra~1/JAGS/JAGS-4.3.1"
           JAGS_MAJOR_VERSION: 4
         run: |
+          options(crayon.enabled = TRUE)
           rcmdcheck::rcmdcheck(
             args        = c("--no-manual", "--as-cran", "--ignore-vignettes"),
             build_args  = c("--no-build-vignettes"),
             error_on    = "warning",
             check_dir   = "check")
-        shell: Rscript {0}"
+        shell: Rscript {0}
 
       - name: Upload check results
         if: failure()
         uses: actions/upload-artifact@main
         with:
           name: ${{ runner.os }}-r${{ matrix.config.r }}-results
-          path: check
\ No newline at end of file
+          path: check
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index b325007c..8b12586d 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -1,87 +1,64 @@
+name: pkgdown
+
 on:
   push:
     branches:
       - main
       - master
     tags:
-      -'*'
-
-name: pkgdown
+      - '*'
 
 jobs:
-  pkgdown:
+  build:
     runs-on: ${{ matrix.config.os }}
-
-    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
-
     strategy:
       fail-fast: false
       matrix:
         config:
-          - {os: ubuntu-20.04,   r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
-
-    env:
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+          - {os: windows-latest, r: 'release'}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
-      - uses: r-lib/actions/setup-r@v2
+      - name: Setup R
+        uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
 
-      - uses: r-lib/actions/setup-pandoc@v2
-
-      - name: Install harfbuzz freetype2 fribidi Headers
-        if: runner.os == 'Linux'
-        run: sudo apt-get install libharfbuzz-dev libfribidi-dev
-
-
-      - name: Query dependencies
+      # Install JAGS on Windows
+      - name: Install JAGS (Windows)
+        if: runner.os == 'Windows'
+        shell: cmd
         run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
-        shell: Rscript {0}
-
-      - name: Restore R package cache
-        uses: actions/cache@v2
-        with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
+          curl -o jags-installer.exe -L https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe/download
+          jags-installer.exe /S
+          del jags-installer.exe
 
-      - name: Install system dependencies
-        if: runner.os == 'Linux'
-        run: |
-          while read -r cmd
-          do
-            eval sudo $cmd
-          done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))')
-      - name: Install JAGS (windows-latest)
+      # Set JAGS environment variables on Windows
+      - name: Set JAGS environment variables (Windows)
         if: runner.os == 'Windows'
-        run: |
-          curl.exe -o wjags.exe --url https://deac-fra.dl.sourceforge.net/project/mcmc-jags/JAGS/4.x/Windows/JAGS-4.3.1.exe
-          wjags.exe /S
-          del wjags.exe
         shell: cmd
+        run: |
+          echo JAGS_ROOT=C:\Program Files\JAGS\JAGS-4.3.1>> %GITHUB_ENV%
+          echo JAGS_MAJOR_VERSION=4>> %GITHUB_ENV%
+          echo PATH=C:\Program Files\JAGS\JAGS-4.3.1\bin;%PATH%>> %GITHUB_ENV%
 
-      - name: Install JAGS (macOS-latest)
-        if: runner.os == 'macOS'
-        run : |
-          rm '/usr/local/bin/gfortran'
-          brew install automake jags
-      - name: Install dependencies
+      # Install Pandoc
+      - name: Setup Pandoc
+        uses: r-lib/actions/setup-pandoc@v2
+
+      # Install the package and its dependencies
+      - name: Install required packages
         run: |
-          remotes::install_deps(dependencies = TRUE)
-          install.packages("pkgdown")
+          options(repos = c(CRAN = "https://cloud.r-project.org"))
+          install.packages('devtools')
+          install.packages('pkgdown')
+          install.packages(c('BayesFactor', 'RoBMA', 'runjags', 'rjags', 'rstan', 'scales', 'vdiffr', 'testthat', 'covr', 'pandoc'))
+          devtools::install()
         shell: Rscript {0}
 
-      - name: Install package
-        run: R CMD INSTALL .
-
       - name: Deploy package
         run: |
           git config --local user.email "actions@github.com"
           git config --local user.name "GitHub Actions"
-          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'
\ No newline at end of file
+          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 96439566..ad2229e9 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -1,86 +1,57 @@
+name: test-coverage
+
 on:
   push:
-    branches:
-      - main
-      - master
-      - pre-release
+    branches: [main, master, pre-release]
   pull_request:
-    branches:
-      - main
-      - master
-      - pre-release
-
-name: test-coverage
+    branches: [main, master, pre-release]
 
 jobs:
-  test-coverage:
+  build:
     runs-on: ${{ matrix.config.os }}
-
-    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
-
     strategy:
       fail-fast: false
       matrix:
         config:
           - {os: windows-latest, r: 'release'}
-          #- {os: ubuntu-latest,  r: 'release'}
-          #- {os: macOS-latest,   r: 'release'}
-
-    env:
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
-      - uses: r-lib/actions/setup-r@v2
+      - name: Setup R
+        uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
 
-      - uses: r-lib/actions/setup-pandoc@v2
-
-      - name: Query dependencies
-        run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
-        shell: Rscript {0}
-
-      - name: Restore R package cache
-        uses: actions/cache@v2
-        with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
-
-      - name: Install system dependencies
-        if: runner.os == 'Linux'
+      # Install JAGS on Windows
+      - name: Install JAGS (Windows)
+        if: runner.os == 'Windows'
+        shell: cmd
         run: |
-          while read -r cmd
-          do
-            eval sudo $cmd
-          done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))')
+          curl -o jags-installer.exe -L https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/JAGS-4.3.1.exe/download
+          jags-installer.exe /S
+          del jags-installer.exe
 
-      - name: Install JAGS (windows-latest)
+      # Set JAGS environment variables on Windows
+      - name: Set JAGS environment variables (Windows)
         if: runner.os == 'Windows'
-        run: |
-          curl.exe -o wjags.exe --url https://deac-fra.dl.sourceforge.net/project/mcmc-jags/JAGS/4.x/Windows/JAGS-4.3.1.exe
-          wjags.exe /S
-          del wjags.exe
         shell: cmd
+        run: |
+          echo JAGS_ROOT=C:\Program Files\JAGS\JAGS-4.3.1>> %GITHUB_ENV%
+          echo JAGS_MAJOR_VERSION=4>> %GITHUB_ENV%
+          echo PATH=C:\Program Files\JAGS\JAGS-4.3.1\bin;%PATH%>> %GITHUB_ENV%
 
-      - name: Install JAGS (macOS-latest)
-        if: runner.os == 'macOS'
-        run : |
-          rm '/usr/local/bin/gfortran'
-          brew install automake jags
-
-      - name: Install dependencies
+      # Install the package
+      - name: Install the required packages
         run: |
-          install.packages(c("remotes"))
-          remotes::install_deps(dependencies = TRUE)
-          remotes::install_cran("covr")
+          options(repos = c(CRAN = "https://cloud.r-project.org"))
+          install.packages('devtools')
+          install.packages('rcmdcheck')
+          install.packages(c('BayesFactor', 'RoBMA', 'runjags', 'rjags', 'rstan', 'scales', 'vdiffr', 'testthat', 'covr', 'pandoc'))
+          devtools::install()
         shell: Rscript {0}
 
+      # Run the coverage check
       - name: Test coverage
         run: covr::codecov()
-        shell: Rscript {0}
\ No newline at end of file
+        shell: Rscript {0}
diff --git a/.gitignore b/.gitignore
index 1f64b29e..f1f8ff58 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 .Rhistory
 .RData
 .Ruserdata
+.Rprofile
 do-not-share
 docs
 BayesTools.Rcheck
diff --git a/BayesTools.Rproj b/BayesTools.Rproj
index 497f8bfc..cb843945 100644
--- a/BayesTools.Rproj
+++ b/BayesTools.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 57fe60d0-34a8-40d6-b464-d3e8df30a8b0
 
 RestoreWorkspace: Default
 SaveWorkspace: Default
diff --git a/DESCRIPTION b/DESCRIPTION
index 600396cd..52e9148b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: BayesTools
 Title: Tools for Bayesian Analyses
-Version: 0.2.17
+Version: 0.2.18
 Description: Provides tools for conducting Bayesian analyses and Bayesian model averaging 
     (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, 
     Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains 
@@ -19,7 +19,7 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 SystemRequirements: JAGS >= 4.3.0 (https://mcmc-jags.sourceforge.io/)
 Depends:
     stats
@@ -43,6 +43,7 @@ Suggests:
     rjags,
     runjags,
     BayesFactor,
+    RoBMA,
     rmarkdown
 RdMacros: Rdpack
 VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
index fc623770..49028dbb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -21,6 +21,7 @@ S3method(range,prior)
 S3method(rng,prior)
 S3method(sd,default)
 S3method(sd,prior)
+S3method(update,BayesTools_table)
 S3method(var,default)
 S3method(var,prior)
 export(JAGS_add_priors)
@@ -51,6 +52,8 @@ export(JAGS_summary_table)
 export(JAGS_to_monitor)
 export(Savage_Dickey_BF)
 export(add_column)
+export(as_marginal_inference)
+export(as_mixed_posteriors)
 export(bridgesampling_object)
 export(ccdf)
 export(cdf)
@@ -80,6 +83,7 @@ export(geom_prior)
 export(geom_prior_list)
 export(inclusion_BF)
 export(interpret)
+export(interpret2)
 export(is.prior)
 export(is.prior.PEESE)
 export(is.prior.PET)
@@ -87,6 +91,7 @@ export(is.prior.discrete)
 export(is.prior.factor)
 export(is.prior.independent)
 export(is.prior.meandif)
+export(is.prior.mixture)
 export(is.prior.none)
 export(is.prior.orthonormal)
 export(is.prior.point)
@@ -134,6 +139,7 @@ export(prior_PET)
 export(prior_factor)
 export(prior_informed)
 export(prior_informed_medicine_names)
+export(prior_mixture)
 export(prior_none)
 export(prior_spike_and_slab)
 export(prior_weightfunction)
diff --git a/NEWS.md b/NEWS.md
index 6c2a77e6..c54e2c76 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,13 @@
+# version 0.2.18
+### Features
+- adding `prior_mixture()` function for creating a mixture of prior distributions
+- adding `as_mixed_posteriors()` and `as_marginal_inference()` functions for a single JAGS models (with spike and slab or mixture priors) to enabling tables and figures based on the corresponding output
+- adding `interpret2()` function for another way of creating textual summaries without the need of inference and samples objects
+- speedup and improvements to the `runjags_estimates_table()` function
+
+### Fixes
+- small fixes for expansion of the RoBMA functionality
+
 ## version 0.2.17
 ### Features
 - adding informed prior distributions for dichotomous and time to event outcomes based on Cochrane Database of Systematic Reviews to `prior_informed()` function
diff --git a/R/JAGS-diagnostics.R b/R/JAGS-diagnostics.R
index 64daf8a9..3e08c52c 100644
--- a/R/JAGS-diagnostics.R
+++ b/R/JAGS-diagnostics.R
@@ -51,7 +51,7 @@ JAGS_diagnostics                 <- function(fit, parameter, type, plot_type = "
   check_list(prior_list, "prior_list")
   if(!all(sapply(prior_list, is.prior)))
     stop("'prior_list' must be a list of priors.")
-  check_char(parameter, "parameter", allow_values = names(prior_list))
+  check_char(parameter, "parameter", allow_values = c(names(prior_list), if(any(names(prior_list) == "bias")) c("PET", "PEESE", "omega")))
   if(!is.null(transformations))
     check_char(names(transformations), "names(transformations)", allow_values = parameter)
 
@@ -246,6 +246,8 @@ JAGS_diagnostics_autocorrelation <- function(fit, parameter, plot_type = "base",
     }
   }else if(is.prior.weightfunction(prior_list[[parameter]])){
     model_samples <- model_samples[,paste0("omega", "[", (length(weightfunctions_mapping(list(prior_list[[parameter]]), cuts_only = TRUE)) - 2):1, "]"),drop = FALSE]
+  }else if(parameter == "omega" && !is.null(prior_list[["bias"]])){
+    model_samples <- model_samples[,paste0("omega", "[", 2:(length(weightfunctions_mapping(prior_list[["bias"]][sapply(prior_list[["bias"]], is.prior.weightfunction)], cuts_only = TRUE, one_sided = TRUE)) - 1), "]"),drop = FALSE]
   }else if(is.prior.vector(prior_list[[parameter]])){
     if(prior_list[[parameter]]$parameters[["K"]] > 1){
       model_samples <- model_samples[,paste0(parameter, "[", 1:prior_list[[parameter]]$parameters[["K"]], "]"),drop = FALSE]
@@ -261,8 +263,15 @@ JAGS_diagnostics_autocorrelation <- function(fit, parameter, plot_type = "base",
     model_samples <- model_samples[,parameter,drop = FALSE]
   }
 
-  prior_list      <- prior_list[parameter]
-  parameter_names <- parameter
+  # deal with bias mixture dispatching
+  if(parameter %in% c("PET", "PEESE", "omega") && !is.null(prior_list[["bias"]])){
+    prior_list      <- prior_list[["bias"]]
+    parameter_names <- parameter
+  }else{
+    prior_list      <- prior_list[parameter]
+    parameter_names <- parameter
+  }
+
 
   # mostly adapted from runjags_estimates_table
   # apply transformations
@@ -342,7 +351,7 @@ JAGS_diagnostics_autocorrelation <- function(fit, parameter, plot_type = "base",
   }
 
   # rename weightfunctions factor levels
-  if(any(sapply(prior_list, is.prior.weightfunction))){
+  if(any(sapply(prior_list, is.prior.weightfunction)) && !is.prior.mixture(prior_list)){
     for(par in names(prior_list)[sapply(prior_list, is.prior.weightfunction)]){
       omega_cuts      <- weightfunctions_mapping(prior_list[par], cuts_only = TRUE)
       parameter_names <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
@@ -350,12 +359,17 @@ JAGS_diagnostics_autocorrelation <- function(fit, parameter, plot_type = "base",
     }
   }
 
+  if(is.prior.mixture(prior_list) && parameter == "omega"){
+    omega_cuts      <- weightfunctions_mapping(prior_list[sapply(prior_list, is.prior.weightfunction)], cuts_only = TRUE, one_sided = TRUE)
+    parameter_names <- sapply(2:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
+  }
+
   # attach the relevant attributes
   colnames(model_samples)          <- parameter_names
   attr(model_samples, "chain")     <- do.call(c, samples_chain)
   attr(model_samples, "iter")      <- do.call(c, samples_iter)
   attr(model_samples, "parameter") <- parameter
-  attr(model_samples, "prior")     <- prior_list[[parameter]]
+  attr(model_samples, "prior")     <- if(is.prior.mixture(prior_list)) prior_list else prior_list[[parameter]]
 
   return(model_samples)
 }
diff --git a/R/JAGS-fit.R b/R/JAGS-fit.R
index c07b575d..a248d99c 100644
--- a/R/JAGS-fit.R
+++ b/R/JAGS-fit.R
@@ -149,7 +149,7 @@ JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list
   # parallel vs. not
   if(parallel){
     cl <- parallel::makePSOCKcluster(cores)
-    on.exit(parallel::stopCluster(cl))
+    on.exit(try(parallel::stopCluster(cl)))
     for(i in seq_along(required_packages)){
       parallel::clusterCall(cl, function(x) requireNamespace(required_packages[i]))
     }
@@ -198,9 +198,9 @@ JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list
   if(inherits(fit, "error") & !silent)
     warning(paste0("The model estimation failed with the following error: ", fit$message), immediate. = TRUE)
 
-  if(autofit & !inherits(fit, "error")){
+  if(autofit && !inherits(fit, "error")){
 
-    converged <- JAGS_check_convergence(fit, prior_list, autofit_control[["max_Rhat"]], autofit_control[["min_ESS"]], autofit_control[["max_error"]], autofit_control[["max_SD_error"]])
+    converged <- JAGS_check_convergence(fit, prior_list, autofit_control[["max_Rhat"]], autofit_control[["min_ESS"]], autofit_control[["max_error"]], autofit_control[["max_SD_error"]], fail_fast = TRUE)
 
     while(!converged){
 
@@ -222,13 +222,10 @@ JAGS_fit <- function(model_syntax, data = NULL, prior_list = NULL, formula_list
         break
       }
 
-      converged <- JAGS_check_convergence(fit, prior_list, autofit_control[["max_Rhat"]], autofit_control[["min_ESS"]], autofit_control[["max_error"]], autofit_control[["max_SD_error"]])
-    }
-  }
+      fit <- runjags::add.summary(fit)
 
-  # stop cluster manually
-  if(parallel){
-    parallel::stopCluster(cl)
+      converged <- JAGS_check_convergence(fit, prior_list, autofit_control[["max_Rhat"]], autofit_control[["min_ESS"]], autofit_control[["max_error"]], autofit_control[["max_SD_error"]], fail_fast = TRUE)
+    }
   }
 
   # add information to the fitted object
@@ -260,7 +257,7 @@ JAGS_extend <- function(fit, autofit_control = list(max_Rhat = 1.05, min_ESS = 5
       cores <- length(fit[["mcmc"]])
     }
     cl <- parallel::makePSOCKcluster(cores)
-    on.exit(parallel::stopCluster(cl))
+    on.exit(try(parallel::stopCluster(cl)))
     for(i in seq_along(required_packages)){
       parallel::clusterCall(cl, function(x) requireNamespace(required_packages[i]))
     }
@@ -295,9 +292,10 @@ JAGS_extend <- function(fit, autofit_control = list(max_Rhat = 1.05, min_ESS = 5
   }
 
   start_time <- Sys.time()
+  itteration <- 0
   converged  <- FALSE
 
-  while(!converged){
+  while(!converged & itteration < autofit_control[["restarts"]]){
 
     if(!is.null(autofit_control[["max_time"]]) && difftime(Sys.time(), start_time, units = autofit_control[["max_time"]][["unit"]]) > autofit_control[["max_time"]][["time"]]){
       if(!silent){
@@ -317,12 +315,13 @@ JAGS_extend <- function(fit, autofit_control = list(max_Rhat = 1.05, min_ESS = 5
       break
     }
 
-    converged <- JAGS_check_convergence(fit, prior_list, autofit_control[["max_Rhat"]], autofit_control[["min_ESS"]], autofit_control[["max_error"]], autofit_control[["max_SD_error"]])
-  }
+    converged <- JAGS_check_convergence(fit, prior_list, autofit_control[["max_Rhat"]], autofit_control[["min_ESS"]], autofit_control[["max_error"]], autofit_control[["max_SD_error"]], fail_fast = TRUE)
 
-  # stop cluster manually
-  if(parallel){
-    parallel::stopCluster(cl)
+    # update the refit call
+    if(!converged){
+      itteration <- itteration + 1
+      refit_call$runjags.object <- fit
+    }
   }
 
   # add information to the fitted object
@@ -349,6 +348,9 @@ JAGS_extend <- function(fit, autofit_control = list(max_Rhat = 1.05, min_ESS = 5
 #' @param max_error maximum MCMC error. Defaults to \code{1.01}.
 #' @param max_SD_error maximum MCMC error as the proportion of standard
 #'   deviation of the parameters. Defaults to \code{0.05}.
+#' @param add_parameters vector of additional parameter names that should be used
+#' (only allows removing last, fixed, omega element if omega is tracked manually).
+#' @param fail_fast whether the function should stop after the first failed convergence check.
 #'
 #' @examples \dontrun{
 #' # simulate data
@@ -380,62 +382,118 @@ JAGS_extend <- function(fit, autofit_control = list(max_Rhat = 1.05, min_ESS = 5
 #'
 #' @seealso [JAGS_fit()]
 #' @export
-JAGS_check_convergence <- function(fit, prior_list, max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error = 0.05){
+JAGS_check_convergence <- function(fit, prior_list, max_Rhat = 1.05, min_ESS = 500, max_error = 0.01, max_SD_error = 0.05, add_parameters = NULL, fail_fast = FALSE){
 
   # check input
   if(!inherits(fit, "runjags"))
     stop("'fit' must be a runjags fit")
-  check_list(prior_list, "prior_list")
-  if(any(!sapply(prior_list, is.prior)))
+  check_list(prior_list, "prior_list", allow_NULL = TRUE)
+  if(!is.null(prior_list) && any(!sapply(prior_list, is.prior)))
     stop("'prior_list' must be a list of priors.")
   check_real(max_Rhat,     "max_Rhat",     lower = 1, allow_NULL = TRUE)
   check_real(min_ESS,      "min_ESS",      lower = 0, allow_NULL = TRUE)
   check_real(max_error,    "max_error",    lower = 0, allow_NULL = TRUE)
   check_real(max_SD_error, "max_SD_error", lower = 0, upper = 1, allow_NULL = TRUE)
+  check_char(add_parameters, "add_parameters", check_length = 0, allow_NULL = TRUE)
 
-  fails         <- NULL
-  invisible(utils::capture.output(temp_summary <- suppressWarnings(summary(fit, silent.jags = TRUE))))
+  # extract samples and parameter information
+  mcmc_samples    <- coda::as.mcmc.list(fit)
+  parameter_names <- colnames(mcmc_samples[[1]])
+  parameters_keep <- rep(TRUE, length(parameter_names))
 
   # remove auxiliary and support parameters from the summary
   for(i in seq_along(prior_list)){
     if(is.prior.weightfunction(prior_list[[i]])){
       if(prior_list[[i]][["distribution"]] %in% c("one.sided", "two.sided")){
-        temp_summary <- temp_summary[!grepl("eta", rownames(temp_summary)),,drop=FALSE]
+        parameters_keep[grepl("eta", parameter_names)] <- FALSE
       }
-      temp_summary <- temp_summary[-max(grep("omega", rownames(temp_summary))),,drop=FALSE]
+      parameter_names[max(grep("omega", parameter_names))] <- FALSE
+    }else if(is.prior.mixture(prior_list[[i]]) && any(sapply(prior_list[[i]], is.prior.weightfunction))){
+      parameters_keep[max(grep("omega", parameter_names))] <- FALSE
     }else if(is.prior.point(prior_list[[i]])){
-      temp_summary <- temp_summary[rownames(temp_summary) != names(prior_list)[i],,drop=FALSE]
+      parameters_keep[parameter_names == names(prior_list)[i]] <- FALSE
     }else if(is.prior.simple(prior_list[[i]]) && prior_list[[i]][["distribution"]] == "invgamma"){
-      temp_summary <- temp_summary[rownames(temp_summary) != paste0("inv_",names(prior_list)[i]),,drop=FALSE]
+      parameters_keep[parameter_names == paste0("inv_",names(prior_list)[i])] <- FALSE
+    }else if(is.prior.mixture(prior_list[[i]]) && length(prior_list[[i]]) == 1 && is.prior.point(prior_list[[i]][[1]])){
+      parameters_keep[parameter_names == names(prior_list)[i]] <- FALSE
     }
   }
 
-  # check the convergence
+  # remove indicators/inclusions
+  parameters_keep[grepl("_indicator", parameter_names)] <- FALSE
+  parameters_keep[grepl("_inclusion", parameter_names)] <- FALSE
+
+  if(all(!parameters_keep)){
+    return(TRUE)
+  }
+
+  # remove parameters that are not monitored
+  for(i in seq_along(mcmc_samples)){
+    mcmc_samples[[i]] <- mcmc_samples[[i]][,parameters_keep,drop=FALSE]
+  }
+
+  ### check the convergence
+  fails <- NULL
+
+  # assess R-hat
   if(!is.null(max_Rhat)){
-    temp_Rhat <- max(ifelse(is.na(temp_summary[, "psrf"]), 1, temp_summary[, "psrf"]))
-    if(temp_Rhat > max_Rhat){
-      fails <- c(fails, paste0("R-hat ", round(temp_Rhat, 3), " is larger than the set target (", max_Rhat, ")."))
+    if(length(fit$mcmc) == 1){
+      warning("Only one chain was run. R-hat cannot be computed.", immediate. = TRUE)
+    }else{
+      temp_Rhat <- coda::gelman.diag(mcmc_samples, multivariate = FALSE, autoburnin = FALSE)$psrf
+      temp_Rhat[is.na(temp_Rhat)] <- 1
+      temp_Rhat <- max(temp_Rhat)
+      if(temp_Rhat > max_Rhat){
+        fails <- c(fails, paste0("R-hat ", round(temp_Rhat, 3), " is larger than the set target (", max_Rhat, ")."))
+        if(fail_fast){
+          return(FALSE)
+        }
+      }
     }
   }
 
   if(!is.null(min_ESS)){
-    temp_ESS <- min(ifelse(is.na(temp_summary[, "SSeff"]), Inf, temp_summary[, "SSeff"]))
+    temp_ESS <- coda::effectiveSize(mcmc_samples)
+    temp_ESS[is.nan(temp_ESS) | temp_ESS == 0] <- Inf
+    temp_ESS <- min(temp_ESS)
     if(temp_ESS < min_ESS){
       fails <- c(fails, paste0("ESS ", round(temp_ESS), " is lower than the set target (", min_ESS, ")."))
+      if(fail_fast){
+        return(FALSE)
+      }
     }
   }
 
+  # compute the MCMC error and & SD error
+  if(!(is.null(max_error) && is.null(max_SD_error))){
+    temp_summary <- summary(mcmc_samples, quantiles = NULL)$statistics
+    if(is.null(dim(temp_summary))){
+      temp_summary <- t(temp_summary)
+    }
+  }
+
+
   if(!is.null(max_error)){
-    temp_error    <- max(ifelse(is.na(temp_summary[, "MCerr"]), 0, temp_summary[, "MCerr"]))
+    temp_error    <- temp_summary[,"Time-series SE"]
+    temp_error[is.na(temp_error)] <- 0
+    temp_error    <- max(temp_error)
     if(temp_error > max_error){
       fails <- c(fails, paste0("MCMC error ", round(temp_error, 5), " is larger than the set target (", max_error, ")."))
+      if(fail_fast){
+        return(FALSE)
+      }
     }
   }
 
   if(!is.null(max_SD_error)){
-    temp_error_SD <- max(ifelse(is.na(temp_summary[, "MC%ofSD"]),   0, temp_summary[, "MC%ofSD"]))
-    if(temp_error_SD/100 > max_SD_error){
-      fails <- c(fails, paste0("MCMC SD error ", round(temp_error_SD/100, 4), " is larger than the set target (", max_SD_error, ")."))
+    temp_error_SD <- temp_summary[,"Time-series SE"] / temp_summary[,"SD"]
+    temp_error_SD[is.na(temp_error_SD)] <- 0
+    temp_error_SD <- max(temp_error_SD)
+    if(temp_error_SD > max_SD_error){
+      fails <- c(fails, paste0("MCMC SD error ", round(temp_error_SD, 3), " is larger than the set target (", max_SD_error, ")."))
+      if(fail_fast){
+        return(FALSE)
+      }
     }
   }
 
@@ -468,6 +526,8 @@ JAGS_add_priors           <- function(syntax, prior_list){
     stop("'prior_list' must be a list of priors.")
   .check_JAGS_syntax(syntax)
 
+  # create an empty attribute holder if any data need to be passed with the syntax
+  syntax_attributes <- NULL
 
   # identify parts of the syntax
   opening_bracket <- regexpr("{", syntax, fixed = TRUE)[1]
@@ -476,9 +536,15 @@ JAGS_add_priors           <- function(syntax, prior_list){
 
   # create the priors relevant syntax
   syntax_priors <- .JAGS_add_priors.fun(prior_list)
+  if(!is.null(attr(syntax_priors, "auxiliary_data"))){
+    syntax_attributes <- attr(syntax_priors, "auxiliary_data")
+  }
 
   # merge everything back together
   syntax <- paste0(syntax_start, "\n", syntax_priors, "\n", syntax_end)
+  if(!is.null(attr(syntax_priors, "auxiliary_data"))){
+    attr(syntax, "auxiliary_data") <- syntax_attributes
+  }
 
   return(syntax)
 }
@@ -486,6 +552,7 @@ JAGS_add_priors           <- function(syntax, prior_list){
 .JAGS_add_priors.fun       <- function(prior_list){
 
   syntax_priors <- ""
+  syntax_attributes <- NULL
 
   for(i in seq_along(prior_list)){
 
@@ -501,6 +568,14 @@ JAGS_add_priors           <- function(syntax, prior_list){
 
       syntax_priors <- paste(syntax_priors, .JAGS_prior.spike_and_slab(prior_list[[i]], names(prior_list)[i]))
 
+    }else if(is.prior.mixture(prior_list[[i]])){
+
+      syntax_mixture <- .JAGS_prior.mixture(prior_list[[i]], names(prior_list)[i])
+      syntax_priors  <- paste(syntax_priors, syntax_mixture)
+      if(!is.null(attr(syntax_mixture, "auxiliary_data"))){
+        syntax_attributes <- attr(syntax_mixture, "auxiliary_data")
+      }
+
     }else if(is.prior.factor(prior_list[[i]])){
 
       syntax_priors <- paste(syntax_priors, .JAGS_prior.factor(prior_list[[i]], names(prior_list)[i]))
@@ -516,6 +591,10 @@ JAGS_add_priors           <- function(syntax, prior_list){
     }
   }
 
+  if(length(syntax_attributes) > 0){
+    attr(syntax_priors, "auxiliary_data") <- syntax_attributes
+  }
+
   return(syntax_priors)
 }
 .JAGS_prior.simple         <- function(prior, parameter_name){
@@ -730,12 +809,6 @@ JAGS_add_priors           <- function(syntax, prior_list){
     stop("improper prior provided")
   check_char(parameter_name, "parameter_name")
 
-  if(is.prior.PET(prior[["variable"]]) | is.prior.PEESE(prior[["variable"]]) | is.prior.weightfunction(prior[["variable"]]))
-    stop("Spike and slab functionality is not implemented for publication bias prior distributions.")
-  if(is.prior.spike_and_slab(prior[["variable"]]))
-     stop("Spike and slab prior distribution cannot be nested inside of a spike and slab prior distribution.")
-
-
   prior_variable_list  <- prior["variable"]
   prior_inclusion_list <- prior["inclusion"]
   names(prior_variable_list)  <- paste0(parameter_name, "_variable")
@@ -750,7 +823,186 @@ JAGS_add_priors           <- function(syntax, prior_list){
 
   return(syntax)
 }
+.JAGS_prior.mixture        <- function(prior_list, parameter_name){
+
+  .check_prior_list(prior_list)
+  if(!is.prior.mixture(prior_list))
+    stop("improper prior provided")
+  check_char(parameter_name, "parameter_name")
+
+  if(inherits(prior_list, "prior.bias_mixture")){
+
+    # dispatch between publication bias prior mixture and a standard prior mixture
+    is_PET            <- sapply(prior_list, is.prior.PET)
+    is_PEESE          <- sapply(prior_list, is.prior.PEESE)
+    is_weightfunction <- sapply(prior_list, is.prior.weightfunction)
+    is_none           <- sapply(prior_list, is.prior.none)
+
+    prior_weights <- attr(prior_list, "prior_weights")
+    syntax <- paste0(" bias_indicator ~ dcat(c(", paste0(prior_weights, collapse = ", "), "))\n")
+
+    # if any prior is bias related, the whole component must be dispatching publication bias
+    if(any(!(is_PET | is_PEESE | is_weightfunction | is_none)))
+      stop("Mixture of publication bias and standard priors is not supported.")
 
+    if(any(is_PET)){
+      if(sum(is_PET) > 1) stop("Only one PET style publication bias adjustment is allowed.")
+
+      named_prior_PET <- prior_list[[which(is_PET)]]
+      class(named_prior_PET) <- class(named_prior_PET)[!class(named_prior_PET) %in% "prior.PET"]
+      named_prior_PET <- list("PET_1" = named_prior_PET)
+
+      syntax <- paste0(
+        syntax,
+        .JAGS_add_priors.fun(named_prior_PET),
+        " PET = PET_1 * (bias_indicator == ", which(is_PET), ")\n"
+      )
+    }
+    if(any(is_PEESE)){
+      if(sum(is_PEESE) > 1) stop("Only one PEESE style publication bias adjustment is allowed.")
+
+      named_prior_PEESE <- prior_list[[which(is_PEESE)]]
+      class(named_prior_PEESE) <- class(named_prior_PEESE)[!class(named_prior_PEESE) %in% "prior.PEESE"]
+      named_prior_PEESE <- list("PEESE_1" = named_prior_PEESE)
+
+      syntax <- paste0(
+        syntax,
+        .JAGS_add_priors.fun(named_prior_PEESE),
+        " PEESE = PEESE_1 * (bias_indicator == ", which(is_PEESE), ")\n"
+      )
+    }
+    if(any(is_weightfunction)){
+      # we cannot simulate weights from the mixture distribution directly because
+      # JAGS does not allow complex support for the cumulative simplex parameter
+      # (we could make it on the non-cumulative simplex but it does not give more advantage)
+
+      # create a vector of the alpha parameters
+      alpha <- lapply(prior_list[is_weightfunction], function(x){
+        if(grepl("fixed", x[["distribution"]])){
+          return(x$parameters[["omega"]])
+        }else{
+          return(x$parameters[["alpha"]])
+        }
+      })
+
+      # dispatch the prior distribution on weight parameters
+      syntax <- paste0(
+        syntax,
+        " for(i in 1:", max(lengths(alpha)), "){\n",
+        "   eta[i] ~ dgamma(eta_shape[i, bias_indicator], 1)\n",
+        " }\n"
+      )
+
+      # transform etas into weights (eta2omega JAGS function is in the RoBMA package)
+      syntax <- paste0(syntax, " omega = eta2omega(eta, omega_index[,bias_indicator], eta_index[,bias_indicator], eta_index_max[bias_indicator])\n")
+
+      # add the necessary auxiliary data: omega_index, eta_index, eta_index_max, eta_shape
+      # create the weightfunction mapping for weights
+      omega_index_weighfunction <- weightfunctions_mapping(prior_list[is_weightfunction], one_sided = TRUE)
+      omega_index_weighfunction <- lapply(omega_index_weighfunction, rev)
+      omega_index_weighfunction <- do.call(rbind, omega_index_weighfunction)
+      omega_index <- matrix(0, ncol = ncol(omega_index_weighfunction), length(prior_list))
+      omega_index[is_weightfunction,] <- omega_index_weighfunction
+
+      # in case of fixed weight functions, the omega_index direly corresponds to the fixed weights
+      for(i in seq_along(prior_list)){
+        if(is.prior.weightfunction(prior_list[[i]])){
+          if(grepl("fixed", prior_list[[i]]$distribution)){
+            omega_index[i,] <- prior_list[[i]]$parameters[["omega"]][omega_index[i,]]
+          }
+        }
+      }
+
+      # create the eta to omega mapping
+      # eta_index_max helps dispatching within the eta2omega function
+      # 0  = non-weightfunction, all weights are set to 0
+      # >1 = indicates how many alpha parameters are needed to construct the weightfunction based on the eta_index
+      # -1 = indicates fixed weightfunction, omega index already encoded all weights
+      eta_index     <- matrix(0, nrow = length(prior_list), ncol = max(lengths(alpha)))
+      eta_index_max <- rep(0, length(prior_list))
+      for(i in seq_along(prior_list)){
+        if(is.prior.weightfunction(prior_list[[i]])){
+          if(grepl("fixed", prior_list[[i]]$distribution)){
+            eta_index_max[i] <- -1
+            eta_index[i,]    <- -1
+          }else{
+            temp_index       <- unique(omega_index[i,])
+            eta_index[i,1:length(temp_index)] <- sort(temp_index)
+            eta_index_max[i] <- length(temp_index)
+          }
+        }else{
+          eta_index_max[i] <- 0
+        }
+      }
+
+      # create priors for eta (set alpha to 1 for non-weightfunctions to keep the sampling in the expected area)
+      eta_shape <- matrix(1, nrow = length(prior_list), ncol = max(lengths(alpha)))
+      for(i in seq_along(prior_list)){
+        if(is.prior.weightfunction(prior_list[[i]])){
+          if(!grepl("fixed", prior_list[[i]]$distribution)){
+            temp_shape <- prior_list[[i]]$parameters[["alpha"]]
+            eta_shape[i,1:length(temp_shape)] <- temp_shape
+          }
+        }
+      }
+
+      # paste the matricies directly into JAGS code (simplifies data handling)
+      syntax <- paste0(
+        syntax,
+        .add_JAGS_matrix("omega_index",   t(omega_index)),
+        .add_JAGS_matrix("eta_index",     t(eta_index)),
+        .add_JAGS_vector("eta_index_max", eta_index_max),
+        .add_JAGS_matrix("eta_shape",     t(eta_shape))
+      )
+    }
+
+  }else{
+
+    prior_weights    <- attr(prior_list, "prior_weights")
+    prior_components <- as.list(prior_list)
+    class(prior_components) <- "list"
+    names(prior_components) <- paste0(parameter_name, "_component_", seq_along(prior_components))
+
+    syntax <- paste0(
+      " ", parameter_name, "_indicator ~ dcat(c(", paste0(prior_weights, collapse = ", "), "))\n",
+      sapply(.JAGS_add_priors.fun(prior_components), paste, collapse = "\n"),
+      " ", parameter_name, " = ",  paste0(names(prior_components), " * ", paste0("(", parameter_name, "_indicator == ", seq_along(prior_components), ")"), collapse = " + "), "\n"
+    )
+  }
+
+  return(syntax)
+}
+
+
+.add_JAGS_vector   <- function(name, vector){
+
+  if(!is.vector(vector))
+    stop("vector must be a vector")
+  check_char(name, "name")
+
+  syntax <- paste0(" ", name, " = c(", paste0(vector, collapse = ", "), ")\n")
+
+  return(syntax)
+}
+.add_JAGS_matrix   <- function(name, matrix){
+
+  if(!is.matrix(matrix))
+    stop("matrix must be a matrix")
+  check_char(name, "name")
+
+  syntax <- ""
+
+  # this unfortunatelly cannot be defined on row/column basis
+  # I tried simplifying this before but only possible initialization is elementwise
+  for(i in 1:nrow(matrix)){
+   syntax <- paste0(
+     syntax, " ",
+     paste0(name,"[", i, ",", seq_len(ncol(matrix)), "] = ", matrix[i,], collapse = "; "), "\n"
+   )
+  }
+
+  return(syntax)
+}
 .check_JAGS_syntax <- function(syntax){
 
   check_char(syntax, "syntax", allow_NULL = TRUE)
@@ -837,6 +1089,10 @@ JAGS_get_inits            <- function(prior_list, chains, seed){
 
       temp_inits <- c(temp_inits, .JAGS_init.spike_and_slab(prior_list[[i]], names(prior_list)[i]))
 
+    }else if(is.prior.mixture(prior_list[[i]])){
+
+      temp_inits <- c(temp_inits, .JAGS_init.mixture(prior_list[[i]], names(prior_list)[i]))
+
     }else if(is.prior.factor(prior_list[[i]])){
 
       temp_inits <- c(temp_inits, .JAGS_init.factor(prior_list[[i]], names(prior_list)[i]))
@@ -993,6 +1249,74 @@ JAGS_get_inits            <- function(prior_list, chains, seed){
 
   return(init)
 }
+.JAGS_init.mixture         <- function(prior_list, parameter_name){
+
+  .check_prior_list(prior_list)
+  if(!is.prior.mixture(prior_list))
+    stop("improper prior provided")
+  check_char(parameter_name, "parameter_name")
+
+  if(inherits(prior_list, "prior.bias_mixture")){
+
+    # dispatch between publication bias prior mixture and a standard prior mixture
+    is_PET            <- sapply(prior_list, is.prior.PET)
+    is_PEESE          <- sapply(prior_list, is.prior.PEESE)
+    is_weightfunction <- sapply(prior_list, is.prior.weightfunction)
+    is_none           <- sapply(prior_list, is.prior.none)
+
+    init <- list()
+
+    # if any prior is bias related, the whole component must be dispatching publication bias
+    if(any(!(is_PET | is_PEESE | is_weightfunction | is_none)))
+      stop("Mixture of publication bias and standard priors is not supported.")
+
+    if(any(is_PET)){
+      if(sum(is_PET) > 1) stop("Only one PET style publication bias adjustment is allowed.")
+
+      named_prior_PET <- prior_list[[which(is_PET)]]
+      class(named_prior_PET) <- class(named_prior_PET)[!class(named_prior_PET) %in% "prior.PET"]
+      named_prior_PET <- list("PET_1" = named_prior_PET)
+
+      init <- c(init, .JAGS_get_inits.fun(named_prior_PET))
+    }
+    if(any(is_PEESE)){
+      if(sum(is_PEESE) > 1) stop("Only one PEESE style publication bias adjustment is allowed.")
+
+      named_prior_PEESE <- prior_list[[which(is_PEESE)]]
+      class(named_prior_PEESE) <- class(named_prior_PEESE)[!class(named_prior_PEESE) %in% "prior.PEESE"]
+      named_prior_PEESE <- list("PEESE_1" = named_prior_PEESE)
+
+      init <- c(init, .JAGS_get_inits.fun(named_prior_PEESE))
+    }
+    if(any(is_weightfunction)){
+
+      # find prior with the most alpha parameters and simulate initial values from it
+      alpha <- sapply(prior_list[is_weightfunction], function(x){
+        if(grepl("fixed", x[["distribution"]])){
+          return(length(x$parameters[["omega"]]))
+        }else{
+          return(length(x$parameters[["alpha"]]))
+        }
+      })
+
+      init <- c(init, .JAGS_get_inits.fun(prior_list[is_weightfunction][which.max(alpha)]))
+    }
+
+    init[["bias_indicator"]] <- rng(prior_list, 1, sample_components = TRUE)
+
+  }else{
+
+    prior_components <- as.list(prior_list)
+    class(prior_components) <- "list"
+    names(prior_components) <- paste0(parameter_name, "_component_", seq_along(prior_components))
+
+    init <- .JAGS_get_inits.fun(prior_components)
+    init[[paste0(parameter_name, "_indicator")]] <- rng(prior_list, 1, sample_components = TRUE)
+
+  }
+
+  return(init)
+}
 
 
 #' @title Create list of monitored parameters for 'JAGS' model
@@ -1034,6 +1358,10 @@ JAGS_to_monitor             <- function(prior_list){
 
       monitor <- c(monitor, .JAGS_monitor.spike_and_slab(prior_list[[i]], names(prior_list)[i]))
 
+    }else if(is.prior.mixture(prior_list[[i]])){
+
+      monitor <- c(monitor, .JAGS_monitor.mixture(prior_list[[i]], names(prior_list)[i]))
+
     }else if(is.prior.factor(prior_list[[i]])){
 
       monitor <- c(monitor, .JAGS_monitor.factor(prior_list[[i]], names(prior_list)[i]))
@@ -1122,14 +1450,57 @@ JAGS_to_monitor             <- function(prior_list){
   names(prior_inclusion) <- paste0(parameter_name, "_inclusion")
 
   monitor <- c(
-    parameter_name,
-    JAGS_to_monitor(prior_variable),
+    paste0(parameter_name, "_indicator"),
     JAGS_to_monitor(prior_inclusion),
-    paste0(parameter_name, "_indicator")
+    parameter_name,
+    JAGS_to_monitor(prior_variable)
   )
 
   return(monitor)
 }
+.JAGS_monitor.mixture        <- function(prior_list, parameter_name){
+
+  .check_prior_list(prior_list)
+  if(!is.prior.mixture(prior_list))
+    stop("improper prior provided")
+  check_char(parameter_name, "parameter_name")
+
+  if(inherits(prior_list, "prior.bias_mixture")){
+
+    # dispatch between publication bias prior mixture and a standard prior mixture
+    is_PET            <- sapply(prior_list, is.prior.PET)
+    is_PEESE          <- sapply(prior_list, is.prior.PEESE)
+    is_weightfunction <- sapply(prior_list, is.prior.weightfunction)
+    is_none           <- sapply(prior_list, is.prior.none)
+
+    monitor <- "bias_indicator"
+
+    # if any prior is bias related, the whole component must be dispatching publication bias
+    if(any(!(is_PET | is_PEESE | is_weightfunction | is_none)))
+      stop("Mixture of publication bias and standard priors is not supported.")
+
+    if(any(is_PET)){
+      if(sum(is_PET) > 1) stop("Only one PET style publication bias adjustment is allowed.")
+
+      monitor <- c(monitor, "PET")
+    }
+    if(any(is_PEESE)){
+      if(sum(is_PEESE) > 1) stop("Only one PEESE style publication bias adjustment is allowed.")
+
+      monitor <- c(monitor, "PEESE")
+    }
+    if(any(is_weightfunction)){
+
+      monitor <- c(monitor, "omega")
+    }
+
+  }else{
+
+    monitor <- c(paste0(parameter_name, "_indicator"), parameter_name)
+  }
+
+  return(monitor)
+}
 
 #' @title Check and list 'JAGS' fitting settings
 #'
diff --git a/R/JAGS-formula.R b/R/JAGS-formula.R
index a7126f48..c42a27a1 100644
--- a/R/JAGS-formula.R
+++ b/R/JAGS-formula.R
@@ -91,11 +91,7 @@ JAGS_formula <- function(formula, parameter, data, prior_list){
     for(factor in names(predictors_type[predictors_type == "factor"])){
 
       # select the corresponding prior for the variable
-      if(is.prior.spike_and_slab(prior_list[[factor]])){
-        this_prior <- prior_list[[factor]][["variable"]]
-      }else{
-        this_prior <- prior_list[[factor]]
-      }
+      this_prior <- prior_list[[factor]]
 
       if(is.prior.treatment(this_prior)){
         stats::contrasts(data[,factor]) <- "contr.treatment"
@@ -115,13 +111,9 @@ JAGS_formula <- function(formula, parameter, data, prior_list){
     for(continuous in names(predictors_type[predictors_type == "continuous"])){
 
       # select the corresponding prior for the variable
-      if(is.prior.spike_and_slab(prior_list[[continuous]])){
-        this_prior <- prior_list[[continuous]][["variable"]]
-      }else{
-        this_prior <- prior_list[[continuous]]
-      }
+      this_prior <- prior_list[[continuous]]
 
-      if(is.prior.factor(this_prior)){
+      if(is.prior.factor(this_prior)|| is.prior.discrete(this_prior) || is.prior.PET(this_prior) || is.prior.PEESE(this_prior) || is.prior.weightfunction(this_prior)){
         stop(paste0("Unsupported prior distribution defined for '", continuous, "' continuous variable. See '?prior' for details."))
       }
     }
@@ -155,7 +147,9 @@ JAGS_formula <- function(formula, parameter, data, prior_list){
     terms_indexes[1] <- 0
 
     formula_syntax   <- c(formula_syntax, paste0(parameter, "_intercept"))
-    prior_syntax     <- c(prior_syntax, .JAGS_prior.simple(prior_list[["intercept"]], paste0(parameter, "_intercept")))
+    prior_intercept_list        <- prior_list["intercept"]
+    names(prior_intercept_list) <- paste0(parameter, "_intercept")
+    prior_syntax     <- c(prior_syntax, .JAGS_add_priors.fun(prior_intercept_list))
   }else{
     terms_indexes    <- attr(model_matrix, "assign")
   }
@@ -164,11 +158,7 @@ JAGS_formula <- function(formula, parameter, data, prior_list){
   for(i in unique(terms_indexes[terms_indexes > 0])){
 
     # extract the corresponding prior distribution for a given coefficient
-    if(is.prior.spike_and_slab(prior_list[[model_terms[i]]])){
-      this_prior <- prior_list[[model_terms[i]]][["variable"]]
-    }else{
-      this_prior <- prior_list[[model_terms[i]]]
-    }
+    this_prior <- prior_list[[model_terms[i]]]
 
     # check whether the term is an interaction or not and save the corresponding attributes
     attr(this_prior, "interaction") <- grepl("__xXx__", model_terms[i])
@@ -225,8 +215,21 @@ JAGS_formula <- function(formula, parameter, data, prior_list){
     }
 
     # update the corresponding prior distribution back into the prior list
-    if(is.prior.spike_and_slab(prior_list[[model_terms[i]]])){
-      this_prior -> prior_list[[model_terms[i]]][["variable"]]
+    # (and forward attributes to lower level components in the case of spike and slab and mixture priors)
+    if(is.prior.spike_and_slab(this_prior)){
+      attr(this_prior, "levels")            -> attr(this_prior[["variable"]], "levels")
+      attr(this_prior, "level_names")       -> attr(this_prior[["variable"]], "level_names")
+      attr(this_prior, "interaction")       -> attr(this_prior[["variable"]], "interaction")
+      attr(this_prior, "interaction_terms") -> attr(this_prior[["variable"]], "interaction_terms")
+      this_prior -> prior_list[[model_terms[i]]]
+    }else if(is.prior.mixture(this_prior)){
+      for(p in seq_along(this_prior)){
+        attr(this_prior, "levels")            -> attr(this_prior[[p]], "levels")
+        attr(this_prior, "level_names")       -> attr(this_prior[[p]], "level_names")
+        attr(this_prior, "interaction")       -> attr(this_prior[[p]], "interaction")
+        attr(this_prior, "interaction_terms") -> attr(this_prior[[p]], "interaction_terms")
+      }
+      this_prior -> prior_list[[model_terms[i]]]
     }else{
       this_prior -> prior_list[[model_terms[i]]]
     }
@@ -337,11 +340,7 @@ JAGS_evaluate_formula <- function(fit, formula, parameter, data, prior_list){
     for(factor in names(predictors_type[predictors_type == "factor"])){
 
       # select the corresponding prior in the variable
-      if(is.prior.spike_and_slab(prior_list_formula[[factor]])){
-        this_prior <- prior_list_formula[[factor]][["variable"]]
-      }else{
-        this_prior <- prior_list_formula[[factor]]
-      }
+      this_prior <- prior_list_formula[[factor]]
 
       if(is.factor(data[,factor])){
         if(all(levels(data[,factor]) %in% .get_prior_factor_level_names(this_prior))){
@@ -378,13 +377,9 @@ JAGS_evaluate_formula <- function(fit, formula, parameter, data, prior_list){
     for(continuous in names(predictors_type[predictors_type == "continuous"])){
 
       # select the corresponding prior in the variable
-      if(is.prior.spike_and_slab(prior_list_formula[[continuous]])){
-        this_prior <- prior_list_formula[[continuous]][["variable"]]
-      }else{
-        this_prior <- prior_list_formula[[continuous]]
-      }
+      this_prior <- prior_list_formula[[continuous]]
 
-      if(is.prior.factor(this_prior)){
+      if(is.prior.factor(this_prior)|| is.prior.discrete(this_prior) || is.prior.PET(this_prior) || is.prior.PEESE(this_prior) || is.prior.weightfunction(this_prior)){
         stop(paste0("Unsupported prior distribution defined for '", continuous, "' continuous variable. See '?prior' for details."))
       }
     }
diff --git a/R/JAGS-marglik.R b/R/JAGS-marglik.R
index 678f9fd7..89fc3e51 100644
--- a/R/JAGS-marglik.R
+++ b/R/JAGS-marglik.R
@@ -273,8 +273,12 @@ JAGS_bridgesampling_posterior <- function(posterior, prior_list, add_parameters
       stop("lb' and 'ub' must be numeric vectors.")
   }
 
+  # these are not generally possible because the component indicators are discrete and bridgesampling
+  # package cannot currently deal with them
   if(any(sapply(prior_list, is.prior.spike_and_slab)))
     stop("Marginal likelihood computation for spike and slab priors is not implemented.")
+  if(any(sapply(prior_list, is.prior.mixture)))
+    stop("Marginal likelihood computation for prior mixture priors is not implemented.")
 
   # get information about the specified parameters
   parameters_names <- .JAGS_bridgesampling_posterior_info(prior_list)
@@ -329,10 +333,6 @@ JAGS_bridgesampling_posterior <- function(posterior, prior_list, add_parameters
 
       add_parameter <- .JAGS_bridgesampling_posterior_info.PP(prior_list[[i]])
 
-    }else if(is.prior.spike_and_slab(prior_list[[i]])){
-
-      add_parameter <- .JAGS_bridgesampling_posterior_info.spike_and_slab(prior_list[[i]], names(prior_list)[i])
-
     }else if(is.prior.factor(prior_list[[i]])){
 
       add_parameter <- .JAGS_bridgesampling_posterior_info.factor(prior_list[[i]], names(prior_list)[i])
@@ -496,33 +496,33 @@ JAGS_bridgesampling_posterior <- function(posterior, prior_list, add_parameters
 
   return(parameter)
 }
-.JAGS_bridgesampling_posterior_info.spike_and_slab <- function(prior, parameter_name){
-
-  .check_prior(prior)
-  if(!is.prior.spike_and_slab(prior))
-    stop("improper prior provided")
-  check_char(parameter_name, "parameter_name")
-
-  if(!is.prior.point(prior[["inclusion"]])){
-
-    parameter_variable  <- .JAGS_bridgesampling_posterior_info.simple(prior[["variable"]],  paste0(parameter_name, "_variable"))
-    parameter_inclusion <- .JAGS_bridgesampling_posterior_info.simple(prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
-
-    parameter <- c(parameter_variable, parameter_inclusion)
-
-    attr(parameter, "lb") <- c(attr(parameter_variable, "lb"), attr(parameter_inclusion, "lb"))
-    attr(parameter, "ub") <- c(attr(parameter_variable, "ub"), attr(parameter_inclusion, "ub"))
-
-    names(attr(parameter, "lb")) <- c(names(attr(parameter_variable, "lb")), names(attr(parameter_inclusion, "lb")))
-    names(attr(parameter, "ub")) <- c(names(attr(parameter_variable, "ub")), names(attr(parameter_inclusion, "ub")))
-
-  }else{
-    parameter  <- .JAGS_bridgesampling_posterior_info.simple(prior[["variable"]],  paste0(parameter_name, "_variable"))
-  }
-
-
-  return(parameter)
-}
+# .JAGS_bridgesampling_posterior_info.spike_and_slab <- function(prior, parameter_name){
+#
+#   .check_prior(prior)
+#   if(!is.prior.spike_and_slab(prior))
+#     stop("improper prior provided")
+#   check_char(parameter_name, "parameter_name")
+#
+#   if(!is.prior.point(prior[["inclusion"]])){
+#
+#     parameter_variable  <- .JAGS_bridgesampling_posterior_info.simple(prior[["variable"]],  paste0(parameter_name, "_variable"))
+#     parameter_inclusion <- .JAGS_bridgesampling_posterior_info.simple(prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
+#
+#     parameter <- c(parameter_variable, parameter_inclusion)
+#
+#     attr(parameter, "lb") <- c(attr(parameter_variable, "lb"), attr(parameter_inclusion, "lb"))
+#     attr(parameter, "ub") <- c(attr(parameter_variable, "ub"), attr(parameter_inclusion, "ub"))
+#
+#     names(attr(parameter, "lb")) <- c(names(attr(parameter_variable, "lb")), names(attr(parameter_inclusion, "lb")))
+#     names(attr(parameter, "ub")) <- c(names(attr(parameter_variable, "ub")), names(attr(parameter_inclusion, "ub")))
+#
+#   }else{
+#     parameter  <- .JAGS_bridgesampling_posterior_info.simple(prior[["variable"]],  paste0(parameter_name, "_variable"))
+#   }
+#
+#
+#   return(parameter)
+# }
 
 #' @title Compute marginal likelihood for 'JAGS' priors
 #'
@@ -569,10 +569,6 @@ JAGS_marglik_priors                <- function(samples, prior_list){
 
       marglik <- marglik + .JAGS_marglik_priors.PP(samples, prior_list[[i]])
 
-    }else if(is.prior.spike_and_slab(prior_list[[i]])){
-
-      marglik <- marglik + .JAGS_marglik_priors.spike_and_slab(samples, prior_list[[i]], names(prior_list)[i])
-
     }else if(is.prior.factor(prior_list[[i]])){
 
       marglik <- marglik + .JAGS_marglik_priors.factor(samples, prior_list[[i]], names(prior_list)[i])
@@ -703,24 +699,24 @@ JAGS_marglik_priors                <- function(samples, prior_list){
 
   return(marglik)
 }
-.JAGS_marglik_priors.spike_and_slab <- function(samples, prior, parameter_name){
-
-  .check_prior(prior)
-  if(!is.prior.spike_and_slab(prior))
-    stop("improper prior provided")
-  check_char(parameter_name, "parameter_name")
-
-  marglik <- 0
-  if(!is.prior.point(prior[["inclusion"]])){
-    marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
-  }
-
-  if(samples[[ paste0(if(prior[["variable"]][["distribution"]] == "invgamma") "inv_" else "", parameter_name, "_variable") ]] != 0){
-    marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior[["variable"]], paste0(parameter_name, "_variable"))
-  }
-
-  return(marglik)
-}
+# .JAGS_marglik_priors.spike_and_slab <- function(samples, prior, parameter_name){
+#
+#   .check_prior(prior)
+#   if(!is.prior.spike_and_slab(prior))
+#     stop("improper prior provided")
+#   check_char(parameter_name, "parameter_name")
+#
+#   marglik <- 0
+#   if(!is.prior.point(prior[["inclusion"]])){
+#     marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
+#   }
+#
+#   if(samples[[ paste0(if(prior[["variable"]][["distribution"]] == "invgamma") "inv_" else "", parameter_name, "_variable") ]] != 0){
+#     marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior[["variable"]], paste0(parameter_name, "_variable"))
+#   }
+#
+#   return(marglik)
+# }
 
 #' @rdname JAGS_marglik_priors
 JAGS_marglik_priors_formula <- function(samples, formula_prior_list){
@@ -780,10 +776,6 @@ JAGS_marglik_parameters                <- function(samples, prior_list){
 
       parameters <- c(parameters, .JAGS_marglik_parameters.PP(samples, prior_list[[i]]))
 
-    }else if(is.prior.spike_and_slab(prior_list[[i]])){
-
-      parameters <- c(parameters, .JAGS_marglik_parameters.spike_and_slab(samples, prior_list[[i]], names(prior_list)[i]))
-
     }else if(is.prior.factor(prior_list[[i]])){
 
       parameters <- c(parameters, .JAGS_marglik_parameters.factor(samples, prior_list[[i]], names(prior_list)[i]))
@@ -925,21 +917,21 @@ JAGS_marglik_parameters                <- function(samples, prior_list){
 
   return(parameter)
 }
-.JAGS_marglik_parameters.spike_and_slab <- function(samples, prior, parameter_name){
-
-  .check_prior(prior)
-  if(!is.prior.spike_and_slab(prior))
-    stop("improper prior provided")
-  check_char(parameter_name, "parameter_name")
-
-  parameter <- list()
-  parameter[paste0(parameter_name, "_variable")]  <- .JAGS_marglik_parameters.simple(samples, prior[["variable"]],  paste0(parameter_name, "_variable"))
-  if(!is.prior.point(prior[[parameter_name]][["inclusion"]])){
-    parameter[paste0(parameter_name, "_inclusion")] <- .JAGS_marglik_parameters.simple(samples, prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
-  }
-
-  return(parameter)
-}
+# .JAGS_marglik_parameters.spike_and_slab <- function(samples, prior, parameter_name){
+#
+#   .check_prior(prior)
+#   if(!is.prior.spike_and_slab(prior))
+#     stop("improper prior provided")
+#   check_char(parameter_name, "parameter_name")
+#
+#   parameter <- list()
+#   parameter[paste0(parameter_name, "_variable")]  <- .JAGS_marglik_parameters.simple(samples, prior[["variable"]],  paste0(parameter_name, "_variable"))
+#   if(!is.prior.point(prior[[parameter_name]][["inclusion"]])){
+#     parameter[paste0(parameter_name, "_inclusion")] <- .JAGS_marglik_parameters.simple(samples, prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
+#   }
+#
+#   return(parameter)
+# }
 
 #' @rdname JAGS_marglik_parameters
 JAGS_marglik_parameters_formula      <- function(samples, formula_data_list, formula_prior_list, prior_list_parameters){
diff --git a/R/interpret.R b/R/interpret.R
index ebab153e..67af4a70 100644
--- a/R/interpret.R
+++ b/R/interpret.R
@@ -21,8 +21,14 @@
 #'
 #' @return \code{interpret} returns character.
 #'
+#' @export interpret
+#' @export interpret2
+#' @name interpret
+#'
 #' @seealso [ensemble_inference] [mix_posteriors] [BayesTools_model_tables] [BayesTools_ensemble_tables]
-#' @export
+NULL
+
+#' @rdname interpret
 interpret <- function(inference, samples, specification, method){
 
   # check input
@@ -50,7 +56,33 @@ interpret <- function(inference, samples, specification, method){
   return(output)
 }
 
-.interpret.specification <- function(inference, samples, specification, method){
+#' @rdname interpret
+interpret2                <- function(specification, method = NULL){
+
+  # check input
+  check_list(specification, "specification", check_length = 0)
+  sapply(specification, function(s){
+    check_char(s$inference_name,       "inference_name",        allow_NULL = TRUE)
+    check_char(s$inference_BF_name,    "inference_BF_name",     allow_NULL = TRUE)
+    check_real(s$inference_BF,         "inference_BF",          allow_NULL = TRUE)
+    check_char(s$estimate_name,        "estimate_name",         allow_NULL = TRUE)
+    check_real(s$estimate_samples,     "estimate_samples",      allow_NULL = TRUE, check_length = 0)
+    check_char(s$estimate_units,       "estimate_units",        allow_NULL = TRUE)
+    check_bool(s$estimate_conditional, "estimate_conditional",  allow_NULL = TRUE)
+  })
+  check_char(method, allow_NULL = TRUE)
+
+
+  output <- ""
+
+  for(i in seq_along(specification)){
+    output <- paste0(output, .interpret.specification2(specification[[i]], method), if(i != length(specification)) " ")
+  }
+
+  return(output)
+}
+
+.interpret.specification  <- function(inference, samples, specification, method){
 
   temp_BF <- inference[[specification[["inference"]]]][["BF"]]
   text_BF <- .interpret.BF(temp_BF, if(!is.null(specification[["inference_name"]])) specification[["inference_name"]] else specification[["inference"]],
@@ -66,7 +98,19 @@ interpret <- function(inference, samples, specification, method){
 
   return(paste0(method, " found ", text_BF, ", ", text_par, "."))
 }
-.interpret.BF            <- function(BF, name, BF_name){
+.interpret.specification2 <- function(specification, method){
+
+  text_BF <- .interpret.BF(specification[["inference_BF"]], specification[["inference_name"]], specification[["inference_BF_name"]])
+
+  if(is.null(specification[["estimate_samples"]])){
+    return(paste0(method, " found ", text_BF, "."))
+  }
+
+  text_par <- .interpret.par(specification[["estimate_samples"]], specification[["estimate_name"]], specification[["estimate_units"]], specification[["estimate_conditional"]])
+
+  return(paste0(method, " found ", text_BF, ", ", text_par, "."))
+}
+.interpret.BF             <- function(BF, name, BF_name){
 
   if(abs(log(BF)) > log(10)){
     text <- "strong evidence"
@@ -92,7 +136,7 @@ interpret <- function(inference, samples, specification, method){
 
   return(text)
 }
-.interpret.par           <- function(samples, name, unit, conditional){
+.interpret.par            <- function(samples, name, unit, conditional){
 
   est <- mean(samples)
   CI  <- unname(stats::quantile(samples, probs = c(0.025, 0.975)))
diff --git a/R/marginal-distributions.R b/R/marginal-distributions.R
index 0f63d568..0801a59c 100644
--- a/R/marginal-distributions.R
+++ b/R/marginal-distributions.R
@@ -15,7 +15,7 @@
 #' \code{contrast = "orthonormal"}, and \code{contrast = "independent"} levels
 #' @param use_formula whether the parameter should be evaluated as a part of supplied formula
 #' @param n_samples number of samples to be drawn for the model-averaged
-#' posterior distribution
+#' prior distribution
 #' @inheritParams density.prior
 #'
 #' @return \code{marginal_posterior} returns a named list of mixed marginal posterior
@@ -27,7 +27,7 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
                                n_samples = 10000, ...){
 
   check_list(samples, "samples")
-  if(any(!sapply(samples, inherits, what = "mixed_posteriors")))
+  if(!inherits(samples, "mixed_posteriors"))
     stop("'samples' must be a be an object generated by 'mix_posteriors' function.")
   check_char(parameter, "parameter", allow_values = names(samples))
   if(!is.null(formula) && !is.language(formula))
@@ -204,7 +204,7 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
       # obtain samples information
       models_ind <- do.call(cbind, lapply(c(if(has_intercept) "intercept", model_terms), function(x) attr(samples[[JAGS_parameter_names(x, formula_parameter = formula_parameter)]], "models_ind")))
       sample_ind <- do.call(cbind, lapply(c(if(has_intercept) "intercept", model_terms), function(x) attr(samples[[JAGS_parameter_names(x, formula_parameter = formula_parameter)]], "sample_ind")))
-      if(!all(models_ind[,1] == models_ind) || !all(sample_ind[,1] == sample_ind))
+      if(!inherits(samples, "as_mixed_posteriors") && (!all(models_ind[,1] == models_ind) || !all(sample_ind[,1] == sample_ind)))
         stop("the posterior samples are not alligned across models/draws")
       models_ind <- models_ind[,1]
 
@@ -216,8 +216,15 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
         terms_indexes[1] <- 0
 
         # get model/sample indices and check for scaling factors
-        temp_multiply_by  <- .get_combined_parameter_scaling_factor_matrix(JAGS_parameter_names("intercept", formula_parameter = formula_parameter),
-                                                                           prior_list = prior_list, posterior = posterior_samples_matrix, models_ind = models_ind, nrow = nrow(data))
+        temp_multiply_by <- .get_combined_parameter_scaling_factor_matrix(
+          JAGS_parameter_names("intercept", formula_parameter = formula_parameter),
+          prior_list  = prior_list,
+          posterior   = posterior_samples_matrix,
+          models_ind  = models_ind,
+          nrow        = nrow(data),
+          simple_list = inherits(samples, "as_mixed_posteriors")
+        )
+
 
         marginal_posterior_samples <- temp_multiply_by * matrix(posterior_samples_matrix[,JAGS_parameter_names("intercept", formula_parameter = formula_parameter)],
                                                        nrow = nrow(data), ncol = nrow(posterior_samples_matrix), byrow = TRUE)
@@ -241,7 +248,14 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
           ,drop = FALSE]
 
         # check for scaling factors
-        temp_multiply_by <- .get_combined_parameter_scaling_factor_matrix(JAGS_model_terms[i], prior_list = prior_list, posterior = posterior_samples_matrix, models_ind = models_ind, nrow = nrow(data))
+        temp_multiply_by <- .get_combined_parameter_scaling_factor_matrix(
+          JAGS_model_terms[i],
+          prior_list  = prior_list,
+          posterior   = posterior_samples_matrix,
+          models_ind  = models_ind,
+          nrow        = nrow(data),
+          simple_list = inherits(samples, "as_mixed_posteriors")
+        )
 
         marginal_posterior_samples <- marginal_posterior_samples + temp_multiply_by * (temp_data %*% t(temp_posterior))
 
@@ -317,7 +331,12 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
       if(prior_samples){
 
         ### generate prior samples matrix in the same format as are the posterior samples
-        prior_samples        <- .mix_priors(prior_list = prior_list, n_samples = n_samples)
+        if(inherits(samples, "as_mixed_posteriors")){
+          prior_samples <- .as_mixed_priors(prior_list = prior_list, n_samples = n_samples, conditional = attr(samples, "conditional", exact = TRUE), conditional_rule = attr(samples, "conditional_rule"))
+        }else{
+          prior_samples <- .mix_priors(prior_list = prior_list, n_samples = n_samples)
+        }
+
         for(i in seq_along(prior_samples)){
           # de-name factor levels
           if(priors_info[[i]][["factor"]]){
@@ -334,7 +353,7 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
         # obtain prior_samples information
         models_ind <- do.call(cbind, lapply(c(if(has_intercept) "intercept", model_terms), function(x) attr(prior_samples[[JAGS_parameter_names(x, formula_parameter = formula_parameter)]], "models_ind")))
         sample_ind <- do.call(cbind, lapply(c(if(has_intercept) "intercept", model_terms), function(x) attr(prior_samples[[JAGS_parameter_names(x, formula_parameter = formula_parameter)]], "sample_ind")))
-        if(!all(models_ind[,1] == models_ind) || !all(sample_ind[,1] == sample_ind))
+        if(!inherits(samples, "as_mixed_posteriors") && (!all(models_ind[,1] == models_ind) || !all(sample_ind[,1] == sample_ind)))
           stop("the prior prior_samples are not alligned across models/draws")
         models_ind <- models_ind[,1]
 
@@ -346,8 +365,14 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
           terms_indexes[1] <- 0
 
           # get model/sample indices and check for scaling factors
-          temp_multiply_by  <- .get_combined_parameter_scaling_factor_matrix(JAGS_parameter_names("intercept", formula_parameter = formula_parameter),
-                                                                             prior_list = prior_list, posterior = prior_samples_matrix, models_ind = models_ind, nrow = nrow(data))
+          temp_multiply_by  <- .get_combined_parameter_scaling_factor_matrix(
+            JAGS_parameter_names("intercept", formula_parameter = formula_parameter),
+            prior_list  = prior_list,
+            posterior   = prior_samples_matrix,
+            models_ind  = models_ind,
+            nrow        = nrow(data),
+            simple_list = inherits(samples, "as_mixed_posteriors")
+          )
 
           marginal_prior_samples <- temp_multiply_by * matrix(prior_samples_matrix[,JAGS_parameter_names("intercept", formula_parameter = formula_parameter)],
                                                          nrow = nrow(data), ncol = nrow(prior_samples_matrix), byrow = TRUE)
@@ -371,7 +396,14 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
             ,drop = FALSE]
 
           # check for scaling factors
-          temp_multiply_by <- .get_combined_parameter_scaling_factor_matrix(JAGS_model_terms[i], prior_list = prior_list, posterior = prior_samples_matrix, models_ind = models_ind, nrow = nrow(data))
+          temp_multiply_by <- .get_combined_parameter_scaling_factor_matrix(
+            JAGS_model_terms[i],
+            prior_list   = prior_list,
+            posterior    = prior_samples_matrix,
+            models_ind   = models_ind,
+            nrow         = nrow(data),
+            simple_list  = inherits(samples, "as_mixed_posteriors")
+          )
 
           marginal_prior_samples <- marginal_prior_samples + temp_multiply_by * (temp_data %*% t(temp_prior))
 
@@ -480,7 +512,11 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
     # add prior samples
     if(prior_samples){
 
-      prior_samples <- .mix_priors(prior_list = prior_list, n_samples = n_samples)
+      if(inherits(samples, "as_mixed_posteriors")){
+        prior_samples <- .as_mixed_priors(prior_list = prior_list, n_samples = n_samples, conditional = attr(samples, "conditional", exact = TRUE), conditional_rule = attr(samples, "conditional_rule"))
+      }else{
+        prior_samples <- .mix_priors(prior_list = prior_list, n_samples = n_samples)
+      }
       marginal_prior_samples <- prior_samples[[parameter]]
 
       # transform if factors
@@ -533,15 +569,19 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
   return(marginal_posterior_samples)
 }
 
-.get_combined_parameter_scaling_factor_matrix <- function(term, prior_list, posterior, models_ind, nrow){
+.get_combined_parameter_scaling_factor_matrix <- function(term, prior_list, posterior, models_ind, nrow, simple_list = FALSE){
 
-  model_samples <- table(models_ind)
+  if(simple_list){
+    temp_multiply_by <- .get_parameter_scaling_factor_matrix(term, prior_list, posterior, nrow = nrow, ncol = nrow(posterior))
+  }else{
+    model_samples <- table(models_ind)
 
-  temp_multiply_by <- do.call(cbind, lapply(unique(models_ind), function(m){
-    temp_prior_list <- lapply(prior_list, function(parameter_priors) parameter_priors[[m]])
-    temp_posterior  <- posterior[models_ind == m,,drop=FALSE]
-    return(.get_parameter_scaling_factor_matrix(term, temp_prior_list, temp_posterior, nrow = nrow, ncol = sum(models_ind == m)))
-  }))
+    temp_multiply_by <- do.call(cbind, lapply(unique(models_ind), function(m){
+      temp_prior_list <- lapply(prior_list, function(parameter_priors) parameter_priors[[m]])
+      temp_posterior  <- posterior[models_ind == m,,drop=FALSE]
+      return(.get_parameter_scaling_factor_matrix(term, temp_prior_list, temp_posterior, nrow = nrow, ncol = sum(models_ind == m)))
+    }))
+  }
 
   return(temp_multiply_by)
 }
@@ -668,10 +708,10 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
   models_ind <- NULL
 
   # mix samples
-  for(i in seq_along(priors)[round(prior_probs * n_samples) > 1]){
+  for(i in seq_along(priors)[ceiling(prior_probs * n_samples) >= 1]){
 
     # sample indexes
-    temp_ind <- 1:round(n_samples * prior_probs[i])
+    temp_ind <- 1:ceiling(n_samples * prior_probs[i])
 
     # sample prior
     samples <- c(samples, rng(priors[[i]], length(temp_ind), transform_factor_samples = FALSE))
@@ -680,6 +720,11 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
     models_ind <- c(models_ind, rep(i, length(temp_ind)))
   }
 
+  # assure the correct number of samples
+  samples    <- samples[1:n_samples]
+  sample_ind <- sample_ind[1:n_samples]
+  models_ind <- models_ind[1:n_samples]
+
   samples <- unname(samples)
   attr(samples, "sample_ind") <- sample_ind
   attr(samples, "models_ind") <- models_ind
@@ -718,10 +763,10 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
   models_ind <- NULL
 
   # mix samples
-  for(i in seq_along(priors)[round(prior_probs * n_samples) > 1]){
+  for(i in seq_along(priors)[ceiling(prior_probs * n_samples) > 1]){
 
     # sample indexes
-    temp_ind <- 1:round(n_samples * prior_probs[i])
+    temp_ind <- 1:ceiling(n_samples * prior_probs[i])
 
     if(is.prior.point(priors[[i]]) & is.prior.simple(priors[[i]])){
       # not sampling the priors in case they were imputed (missing dimensions)
@@ -736,6 +781,11 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
     models_ind <- c(models_ind, rep(i, length(temp_ind)))
   }
 
+  # assure the correct number of samples
+  samples    <- samples[1:n_samples,,drop=FALSE]
+  sample_ind <- sample_ind[1:n_samples]
+  models_ind <- models_ind[1:n_samples]
+
   rownames(samples) <- NULL
   colnames(samples) <- paste0(parameter,"[",1:K,"]")
   attr(samples, "sample_ind") <- sample_ind
@@ -902,10 +952,10 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
   models_ind <- NULL
 
   # mix samples
-  for(i in seq_along(priors)[round(prior_probs * n_samples) > 1]){
+  for(i in seq_along(priors)[ceiling(prior_probs * n_samples) > 1]){
 
     # sample indexes
-    temp_ind <- 1:round(n_samples * prior_probs[i])
+    temp_ind <- 1:ceiling(n_samples * prior_probs[i])
 
     if(is.prior.none(priors[[i]])){
       samples <- rbind(samples, matrix(1, ncol = length(omega_cuts) - 1, nrow = length(temp_ind)))
@@ -920,6 +970,11 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
     models_ind <- c(models_ind, rep(i, length(temp_ind)))
   }
 
+  # assure the correct number of samples
+  samples    <- samples[1:n_samples,,drop=FALSE]
+  sample_ind <- sample_ind[1:n_samples]
+  models_ind <- models_ind[1:n_samples]
+
   rownames(samples) <- NULL
   colnames(samples) <- omega_names
   attr(samples, "sample_ind") <- sample_ind
@@ -931,6 +986,428 @@ marginal_posterior <- function(samples, parameter, formula = NULL, at = NULL, pr
   return(samples)
 }
 
+.as_mixed_priors            <- function(prior_list, seed = NULL, n_samples = 10000, conditional = NULL, conditional_rule = NULL){
+
+  check_list(prior_list, "prior_list")
+  if(any(!sapply(prior_list, is.prior)))
+    stop("'prior_list' must be a list of prior distributions")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  # set seed only once at the beginning -- not in the individual draws as the priors will end up completely correlated
+  if(is.null(seed)){
+    seed <- sample(.Machine$integer.max, 1)
+  }
+  set.seed(seed)
+
+  # adapted from 'as_mixed_posteriors'
+  parameters <- names(prior_list)
+  out        <- list()
+
+  # estimate the number of necessary samples for conditioning
+  if(length(conditional) > 0){
+
+    conditioning_probabilities <- sapply(conditional, function(parameter){
+
+      temp_prior <- prior_list[[parameter]]
+
+      if(is.prior.spike_and_slab(temp_prior)){
+
+        return(mean(temp_prior[["inclusion"]]))
+
+      }else if(is.prior.mixture(temp_prior)){
+
+        components    <- attr(temp_prior, "components")
+        prior_weights <- attr(temp_prior, "prior_weights")
+
+        if(!all(components %in% c("null", "alternative")))
+          stop("conditional mixture posterior distributions are available only for 'null' and 'alternative' components")
+
+        return(sum(prior_weights[components == "alternative"]) / sum(prior_weights))
+
+      }else{
+
+        warning(sprintf("The parameter '%s' is not a conditional parameter.", parameter), call. = FALSE, immediate. = TRUE)
+        return(1)
+      }
+    })
+
+    # multiply by 1.25 to ensure that the requested number of samples is reached
+    requested_samples <- n_samples
+    if(conditional_rule == "AND"){
+      n_samples <- round(n_samples / prod(conditioning_probabilities) * 1.25)
+    }else if(conditional_rule == "OR"){
+      n_samples <- round(n_samples / (1 - prod(1 - conditioning_probabilities)) * 1.25)
+    }
+  }
+
+  # create the samples
+  for(p in seq_along(parameters)){
+
+    # prepare parameter specific values
+    temp_parameter <- parameters[p]
+    temp_prior     <- prior_list[[temp_parameter]]
+
+    if(is.prior.spike_and_slab(temp_prior)){
+      # spike and slab priors
+      out[[temp_parameter]] <- .as_mixed_priors.spike_and_slab(temp_prior, temp_parameter, NULL, n_samples)
+
+    }else if(is.prior.mixture(temp_prior)){
+      # mixture priors
+      out[[temp_parameter]] <- .as_mixed_priors.mixture(temp_prior, temp_parameter, NULL, n_samples)
+
+    }else if(is.prior.weightfunction(temp_prior)){
+      # weightfunctions:
+      out[[temp_parameter]] <- .as_mixed_priors.weightfunction(temp_prior, temp_parameter, NULL, n_samples)
+
+    }else if(is.prior.factor(temp_prior)){
+      # factor priors
+      out[[temp_parameter]] <- .as_mixed_priors.factor(temp_prior, temp_parameter, NULL, n_samples)
+
+    }else if(is.prior.vector(temp_prior)){
+      # vector priors:
+      out[[temp_parameter]] <- .as_mixed_priors.vector(temp_prior, temp_parameter, NULL, n_samples)
+
+    }else if(is.prior.simple(temp_prior)){
+      # simple priors:
+      out[[temp_parameter]] <- .as_mixed_priors.simple(temp_prior, temp_parameter, NULL, n_samples)
+
+    }else{
+      stop("The posterior samples cannot be mixed: unsupported mixture of prior distributions.")
+    }
+
+    # add formula relevant information
+    if(!is.null(attr(temp_prior, which = "parameter"))){
+      class(out[[temp_parameter]]) <- c(class(out[[temp_parameter]]), "mixed_posteriors.formula")
+      attr(out[[temp_parameter]], "formula_parameter")  <- attr(temp_prior, which = "parameter")
+    }
+  }
+
+
+  # perform conditioning (and copy back with attributes)
+  if(length(conditional) > 0){
+
+    # obtain the indicator samples
+    conditioning_samples <- do.call(cbind, lapply(conditional, function(parameter){
+
+      temp_prior <- prior_list[[parameter]]
+
+      if(is.prior.spike_and_slab(temp_prior)){
+
+        return(attr(out[[parameter]], "models_ind") == 1)
+
+      }else if(is.prior.mixture(temp_prior)){
+
+        components <- attr(temp_prior, "components")
+        return(attr(out[[parameter]], "models_ind") %in% which(components == "alternative"))
+
+      }else{
+
+        return(rep(TRUE, n_samples))
+      }
+    }))
+    conditioning_samples <- apply(conditioning_samples, 1, ifelse(conditional_rule == "AND", all, any))
+
+    # check enough samples were drawn (if too many remove the extra ones)
+    if(sum(conditioning_samples) < requested_samples){
+      warning(sprintf("Only %d samples were drawn from the prior distributions due to conditioning.", sum(conditioning_samples)))
+    }else{
+      conditioning_samples[which(conditioning_samples)[-(1:requested_samples)]] <- FALSE
+    }
+
+    # select the conditional samples (and copy attributes)
+    for(p in seq_along(parameters)){
+      temp <- attributes(out[[parameters[p]]])
+      if(is.null(dim(out[[parameters[p]]]))){
+        out[[parameters[p]]] <- out[[parameters[p]]][conditioning_samples]
+        attributes(out[[parameters[p]]]) <- c(attributes(out[[parameters[p]]]), temp)
+        attr(out[[parameters[p]]], "models_ind") <- attr(out[[parameters[p]]], "models_ind")[conditioning_samples]
+      }else{
+        out[[parameters[p]]] <- out[[parameters[p]]][conditioning_samples,,drop=FALSE]
+        attributes(out[[parameters[p]]]) <- c(attributes(out[[parameters[p]]])[!names(attributes(out[[parameters[p]]])) %in% c("dimnames")], temp[!names(temp) %in% c("dim")])
+        attr(out[[parameters[p]]], "models_ind") <- attr(out[[parameters[p]]], "models_ind")[conditioning_samples]
+      }
+    }
+  }
+
+  return(out)
+}
+.as_mixed_priors.simple         <- function(prior, parameter, seed = NULL, n_samples = 10000){
+
+  # check input
+  check_list(prior, "prior")
+  check_char(parameter, "parameter")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  # do not set seed when sampling multiple prior for the same model -- they will end up completely correlated
+  if(!is.null(seed)){
+    set.seed(seed)
+  }
+
+  # prepare output objects
+  samples <- rng(prior, n_samples, transform_factor_samples = FALSE)
+
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- FALSE
+  attr(samples, "parameter")  <- parameter
+  attr(samples, "prior_list") <- prior
+  class(samples) <- c("mixed_posteriors", "mixed_posteriors.simple")
+
+  return(samples)
+}
+.as_mixed_priors.vector         <- function(prior, parameter, seed = NULL, n_samples = 10000){
+
+  # check input
+  check_list(prior, "prior")
+  check_char(parameter, "parameter")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  # do not set seed when sampling multiple prior for the same model -- they will end up completely correlated
+  if(!is.null(seed)){
+    set.seed(seed)
+  }
+
+  # prepare output objects
+  K <- prior$parameters[["K"]]
+
+  if(is.prior.point(prior) & is.prior.simple(prior)){
+    # not sampling the prior in case they were imputed (missing dimensions)
+    samples <- matrix(prior$parameters[["location"]], nrow = n_samples, ncol = K)
+  }else if(K == 1){
+    samples <- matrix(rng(prior, n_samples, transform_factor_samples = FALSE), nrow = n_samples, ncol = K)
+  }else{
+    samples <- rng(prior, n_samples, transform_factor_samples = FALSE)
+  }
+
+  rownames(samples) <- NULL
+  colnames(samples) <- paste0(parameter,"[",1:K,"]")
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- FALSE
+  attr(samples, "parameter")  <- parameter
+  attr(samples, "prior_list") <- prior
+  class(samples) <- c("mixed_posteriors", "mixed_posteriors.vector")
+
+  return(samples)
+}
+.as_mixed_priors.factor         <- function(prior, parameter, seed = NULL, n_samples = 10000){
+
+  # check input
+  check_list(prior, "prior")
+  check_char(parameter, "parameter")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  # check the prior levels
+  levels <- .get_prior_factor_levels(prior)
+
+  # gather and check compatibility of prior distributions
+  prior_info <- list(
+    "levels"      = .get_prior_factor_levels(prior),
+    "level_names" = .get_prior_factor_level_names(prior),
+    "interaction" = .is_prior_interaction(prior),
+    "treatment"   = is.prior.treatment(prior),
+    "independent" = is.prior.independent(prior),
+    "orthonormal" = is.prior.orthonormal(prior),
+    "meandif"     = is.prior.meandif(prior)
+  )
+
+  if(prior_info[["treatment"]]){
+
+    if(levels == 1){
+
+      samples <- .as_mixed_priors.simple(prior, parameter, seed, n_samples)
+      samples <- matrix(samples, ncol = 1)
+
+    }else{
+
+      samples <- lapply(1:levels, function(i) .as_mixed_priors.simple(prior, paste0(parameter, "[", i, "]"), seed, n_samples))
+      samples <- do.call(cbind, samples)
+
+    }
+
+    rownames(samples) <- NULL
+    colnames(samples) <- paste0(parameter,"[",prior_info$level_names[-1],"]")
+    attr(samples, "sample_ind") <- FALSE
+    attr(samples, "models_ind") <- FALSE
+    attr(samples, "parameter")  <- parameter
+    attr(samples, "prior_list") <- prior
+    class(samples) <- c("mixed_posteriors", "mixed_posteriors.factor", "mixed_posteriors.vector")
+
+  }else if(prior_info[["independent"]]){
+
+    if(levels == 1){
+
+      samples <- .as_mixed_priors.simple(prior, parameter, seed, n_samples)
+      samples <- matrix(samples, ncol = 1)
+
+    }else{
+
+      samples <- lapply(1:levels, function(i) .as_mixed_priors.simple(prior, paste0(parameter, "[", i, "]"), seed, n_samples))
+      samples <- do.call(cbind, samples)
+
+    }
+
+    rownames(samples) <- NULL
+    colnames(samples) <- paste0(parameter,"[",prior_info$level_names,"]")
+    attr(samples, "sample_ind") <- FALSE
+    attr(samples, "models_ind") <- FALSE
+    attr(samples, "parameter")  <- parameter
+    attr(samples, "prior_list") <- prior
+    class(samples) <- c("mixed_posteriors", "mixed_posteriors.factor", "mixed_posteriors.vector")
+
+  }else if(prior_info[["orthonormal"]] | prior_info[["meandif"]]){
+
+    prior$parameters[["K"]] <- levels
+    samples <- .as_mixed_priors.vector(prior, parameter, seed, n_samples)
+    class(samples) <- c(class(samples), "mixed_posteriors.factor")
+
+  }
+
+  attr(samples, "levels")      <- prior_info[["levels"]]
+  attr(samples, "level_names") <- prior_info[["level_names"]]
+  attr(samples, "interaction") <- prior_info[["interaction"]]
+  attr(samples, "treatment")   <- prior_info[["treatment"]]
+  attr(samples, "independent") <- prior_info[["independent"]]
+  attr(samples, "orthonormal") <- prior_info[["orthonormal"]]
+  attr(samples, "meandif")     <- prior_info[["meandif"]]
+
+  return(samples)
+}
+.as_mixed_priors.weightfunction <- function(prior, parameter, seed = NULL, n_samples = 10000){
+
+  # check input
+  check_list(prior, "prior")
+  check_char(parameter, "parameter")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  # do not set seed when sampling multiple prior for the same model -- they will end up completely correlated
+  if(!is.null(seed)){
+    set.seed(seed)
+  }
+
+  # obtain mapping for the weight coefficients
+  omega_mapping <- weightfunctions_mapping(list(prior))
+  omega_cuts    <- weightfunctions_mapping(list(prior), cuts_only = TRUE)
+  omega_names   <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
+
+  # prepare output objects
+  samples <- rng(prior, n_samples)
+
+  rownames(samples) <- NULL
+  colnames(samples) <- omega_names
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- FALSE
+  attr(samples, "parameter")  <- parameter
+  attr(samples, "prior_list") <- prior
+  class(samples) <- c("mixed_posteriors", "mixed_posteriors.weightfunction")
+
+  return(samples)
+}
+.as_mixed_priors.spike_and_slab <- function(prior, parameter, seed = NULL, n_samples = 10000){
+
+  # check input
+  check_list(prior, "prior")
+  check_char(parameter, "parameter")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  # do not set seed when sampling multiple prior for the same model -- they will end up completely correlated
+  if(!is.null(seed)){
+    set.seed(seed)
+  }
+
+  prior_variable   <- prior[["variable"]]
+  prior_inclusion  <- prior[["inclusion"]]
+
+  inclusion <- stats::rbinom(n_samples, size = 1, prob = rng(prior_inclusion, n_samples))
+
+  if(is.prior.factor(prior_variable)){
+
+    samples <- .as_mixed_priors.factor(prior_variable, parameter, seed, n_samples)
+
+  }else if(is.prior.simple(prior_variable)){
+
+    samples <- .as_mixed_priors.simple(prior_variable, parameter, seed, n_samples)
+
+  }
+
+  # merge with names and attributes
+  samples       <- samples * inclusion
+
+  class(samples) <- c(class(samples), "mixed_posteriors.spike_and_slab")
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- inclusion
+  attr(samples, "prior_list") <- prior
+
+  return(samples)
+}
+.as_mixed_priors.mixture        <- function(prior, parameter, seed = NULL, n_samples = 10000){
+
+  # check input
+  check_list(prior, "prior")
+  check_char(parameter, "parameter")
+  check_real(seed, "seed", allow_NULL = TRUE)
+  check_int(n_samples, "n_samples")
+
+  is_PET            <- sapply(prior, is.prior.PET)
+  is_PEESE          <- sapply(prior, is.prior.PEESE)
+  is_weightfunction <- sapply(prior, is.prior.weightfunction)
+
+  if(any(is_PET | is_PEESE | is_weightfunction)){
+
+    stop("not implemented yet")  # probably not needed
+    # samples <- NULL
+    #
+    # if(any(is_PET)){
+    #   samples <- cbind(samples, .as_mixed_posteriors.simple(fit, prior[is_PET][[1]], "PET"))
+    # }
+    # if(any(is_PEESE)){
+    #   samples <- cbind(.as_mixed_posteriors.simple(fit, prior[is_PEESE][[1]], "PEESE"))
+    # }
+    # if(any(is_weightfunction)){
+    #   # create a dummy prior with all the cuts
+    #   dummy_prior <- #TODO:
+    #   samples     <- cbind(.as_mixed_posteriors.weightfunction(fit, dummy_prior, "omega"))
+    # }
+    #
+    # samples <- .as_mixed_posteriors.factor(fit, prior_variable, parameter)
+    # attr(samples, "models_ind") <- as.vector(model_samples[,paste0(parameter, "_indicator")])
+
+  }else{
+
+    is_simple <- sapply(prior, is.prior.simple)
+    is_factor <- sapply(prior, is.prior.factor)
+
+    if(any(is_factor)){
+      temp_samples <- .mix_priors.factor(prior, parameter = parameter, seed = seed, n_samples = n_samples)
+    }else{
+      temp_samples <- .mix_priors.simple(prior, parameter = parameter, seed = seed, n_samples = n_samples)
+    }
+
+  }
+
+  # the samples parameters need to be randomly shuffled
+  # (the  .mix_priors.XXX functions generate the samples model by model to keep bridge-sampling model-averaging consistent structure,
+  #  this however does not apply to the spike an slab priors)
+  random_ind <- sample(n_samples)
+  if(is.null(dim(temp_samples))){
+    samples <- temp_samples[random_ind]
+  }else{
+    samples <- temp_samples[random_ind,,drop=FALSE]
+  }
+  attributes(samples) <- attributes(temp_samples)
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- attr(samples, "models_ind")[random_ind]
+
+  # append classes and priors
+  class(samples) <- c(class(samples), "mixed_posteriors.mixture")
+  attr(samples, "prior_list") <- prior
+
+  return(samples)
+}
 
 #' @title Compute Savage-Dickey inclusion Bayes factors
 #'
@@ -1067,8 +1544,7 @@ Savage_Dickey_BF <- function(posterior, null_hypothesis = 0, normal_approximatio
 #' @inheritParams marginal_posterior
 #' @inheritParams Savage_Dickey_BF
 #'
-#' @return \code{mix_posteriors} returns a named list of mixed posterior
-#' distributions (either a vector of matrix).
+#' @return \code{marginal_inference} returns an object of class 'marginal_inference'.
 #'
 #' @seealso [ensemble_inference] [mix_posteriors] [BayesTools_ensemble_tables]
 #'
@@ -1080,13 +1556,14 @@ marginal_inference <- function(model_list, marginal_parameters, parameters, is_n
   # check input (majority of the checks performed within mix_posteriors)
   check_list(model_list, "model_list")
   check_char(parameters, "parameters", check_length = FALSE)
+  check_char(marginal_parameters, "marginal_parameters", check_length = FALSE)
   check_list(is_null_list, "is_null_list", check_length = length(parameters))
   if(!all(unlist(sapply(model_list, function(m) sapply(attr(m[["fit"]], "prior_list"), function(p) is.prior(p))))))
     stop("model_list:priors must contain 'BayesTools' priors")
 
 
   # create one full model-averaged ensemble
-  averaged_posterior <- BayesTools::mix_posteriors(
+  averaged_posterior <- mix_posteriors(
     model_list   = model_list,
     parameters   = parameters,
     is_null_list = is_null_list,
@@ -1111,7 +1588,7 @@ marginal_inference <- function(model_list, marginal_parameters, parameters, is_n
 
     # obtain model-averaged posterior conditional on including the parameter of interest
     # (different from individual conditionals)
-    temp_conditional_posterior <- BayesTools::mix_posteriors(
+    temp_conditional_posterior <- mix_posteriors(
       model_list   = model_list[!is_null_list[[marginal_parameters[i]]]],
       parameters   = parameters,
       is_null_list = lapply(is_null_list, function(l) l[!is_null_list[[marginal_parameters[i]]]]),
@@ -1150,3 +1627,102 @@ marginal_inference <- function(model_list, marginal_parameters, parameters, is_n
   class(out) <- c(class(out), "marginal_inference")
   return(out)
 }
+
+
+#' @title Model-average marginal posterior distributions and
+#' marginal Bayes factors based on BayesTools JAGS model via \code{marginal_inference}
+#'
+#' @description Creates marginal model-averaged and conditional
+#' posterior distributions based on a BayesTools JAGS model, vector of parameters,
+#' formula, and a list of conditional specifications for each parameter.
+#' Computes inclusion Bayes factors for each marginal estimate via a Savage-Dickey
+#' density approximation.
+#'
+#' @param marginal_parameters parameters for which the the marginal summary
+#' should be created
+#' @param conditional_list list of conditional parameters for each marginal parameter
+#' @param parameters all parameters included in the model_list that are
+#' relevant for the formula (all of which need to have specification of
+#' \code{is_null_list})
+#' @inheritParams as_mixed_posteriors
+#' @inheritParams marginal_inference
+#' @inheritParams Savage_Dickey_BF
+#'
+#' @return \code{as_marginal_inference} returns an object of class 'marginal_inference'.
+#'
+#' @seealso [marginal_inference] [as_mixed_posteriors]
+#'
+#' @export
+as_marginal_inference <- function(model, marginal_parameters, parameters, conditional_list, conditional_rule, formula,
+                                  null_hypothesis = 0, normal_approximation = FALSE,
+                                  n_samples = 10000, silent = FALSE, force_plots = FALSE){
+
+  # check input (majority of the checks performed within mix_posteriors)
+  # check input
+  if(!inherits(model, "BayesTools_fit"))
+    stop("'model' must be a 'BayesTools_fit'")
+  check_char(parameters, "parameters", check_length = FALSE)
+  check_char(marginal_parameters, "marginal_parameters", check_length = FALSE)
+  check_list(conditional_list, "conditional_list", check_length = length(marginal_parameters))
+  check_char(conditional_rule, "conditional_rule")
+
+  priors <- attr(model, "prior_list")
+
+
+  # create one full model-averaged ensemble
+  averaged_posterior <- as_mixed_posteriors(
+    model        = model,
+    parameters   = parameters
+  )
+
+  # prepare output object
+  out <- list(
+    conditional = list(),
+    averaged    = list(),
+    inference   = list()
+  )
+
+  for(i in seq_along(marginal_parameters)){
+
+    check_char(conditional_list[[marginal_parameters[i]]], sprintf("conditional_list[[%1$s]]", marginal_parameters[i]), check_length = FALSE, allow_values = parameters, allow_NULL = TRUE)
+
+    # obtain model-averaged posterior conditional on including the parameter of interest
+    # (different from individual conditionals)
+    temp_conditional_posterior <- as_mixed_posteriors(
+      model            = model,
+      parameters       = parameters,
+      conditional      = conditional_list[[marginal_parameters[i]]],
+      conditional_rule = conditional_rule,
+      force_plots       = force_plots
+    )
+
+    # compute the marginals
+    out[["averaged"]][[marginal_parameters[i]]] <- marginal_posterior(
+      samples           = averaged_posterior,
+      parameter         = marginal_parameters[i],
+      formula           = formula,
+      prior_samples     = TRUE,
+      n_samples         = n_samples
+    )
+    out[["conditional"]][[marginal_parameters[i]]] <- marginal_posterior(
+      samples           = temp_conditional_posterior,
+      parameter         = marginal_parameters[i],
+      formula           = formula,
+      prior_samples     = TRUE,
+      n_samples         = n_samples
+    )
+
+    # and inclusion Bayes factor
+    out[["inference"]][[marginal_parameters[i]]] <- Savage_Dickey_BF(
+      posterior            = out[["conditional"]][[marginal_parameters[i]]],
+      null_hypothesis      = null_hypothesis,
+      normal_approximation = normal_approximation,
+      silent               = silent
+    )
+  }
+
+  attr(out, "null_hypothesis")      <- null_hypothesis
+  attr(out, "normal_approximation") <- normal_approximation
+  class(out) <- c(class(out), "marginal_inference")
+  return(out)
+}
diff --git a/R/model-averaging-plots.R b/R/model-averaging-plots.R
index cd72e180..e74df5b8 100644
--- a/R/model-averaging-plots.R
+++ b/R/model-averaging-plots.R
@@ -366,6 +366,15 @@ plot_prior_list <- function(prior_list, plot_type = "base",
   prior_list     <- prior_list[round(n_samples * mixing_prop) > 0]
   mixing_prop    <- mixing_prop[round(n_samples * mixing_prop) > 0]
 
+  # replace non-weighfunctions from prior mixture feneration
+  if(any(!c(sapply(prior_list, is.prior.weightfunction) | sapply(prior_list, is.prior.none)))){
+    for(i in seq_along(prior_list)){
+      if(!(is.prior.weightfunction(prior_list[[i]]) | is.prior.none(prior_list[[i]]))){
+        prior_list[[i]] <- prior_none(prior_weights = prior_weights[i])
+      }
+    }
+  }
+
   # get the samples
   samples_list <- list()
   for(i in seq_along(prior_list)){
@@ -487,6 +496,29 @@ plot_prior_list <- function(prior_list, plot_type = "base",
 }
 .plot_data_prior_list.simple         <- function(prior_list, x_seq, x_range, x_range_quant, n_points, n_samples, force_samples, individual,
                                                  transformation, transformation_arguments, transformation_settings){
+
+  # dispatching for spike and slab priors
+  if(is.prior.spike_and_slab(prior_list)){
+
+    prior_inclusion   <- prior_list[["inclusion"]]
+    prior_variable    <- prior_list[["variable"]]
+
+    if(mean(prior_inclusion) < 1 && mean(prior_inclusion) > 0){
+      # create a dummy list for the simple mixture
+      prior_null                        <- prior("spike", list(0), prior_weights = 1-mean(prior_inclusion))
+      prior_variable[["prior_weights"]] <- mean(prior_inclusion)
+
+      prior_list <- list(
+        prior_variable,
+        prior_null
+      )
+    }else if(mean(prior_inclusion) >= 1){
+      prior_list <- list(prior_variable)
+    }else if(mean(prior_inclusion) <= 0){
+      prior_list <- list(prior("spike", list(0)))
+    }
+  }
+
   # join the same priors
   prior_list <- .simplify_prior_list(prior_list)
 
@@ -608,6 +640,10 @@ plot_prior_list <- function(prior_list, plot_type = "base",
 
 .simplify_prior_list <- function(prior_list){
 
+  if(is.prior.mixture(prior_list)){
+    class(prior_list) <- NULL
+  }
+
   # return the input with fewer than 2 priors
   if(length(prior_list) < 2){
     return(prior_list)
@@ -922,7 +958,16 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE
 
     # add priors, if requested
     if(prior){
-      prior_list      <- attr(samples[[parameter]], "prior_list")
+
+      # extract the correct weightfunction samples
+      if(!is.null(samples[[parameter]])){
+        prior_list <- attr(samples[[parameter]], "prior_list")
+      }else if(!is.null(samples[["bias"]])){
+        prior_list <- attr(samples[["bias"]], "prior_list")
+      }else{
+        stop("No 'omega' or 'bias' samples found.")
+      }
+
       prior_list      <- .simplify_prior_list(prior_list)
       plot_data_prior <- .plot_data_prior_list.weightfunction(prior_list, x_seq = NULL, x_range = xlim, x_range_quant = NULL,
                                                               n_points = n_points, n_samples = n_samples)
@@ -1274,6 +1319,7 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE
 .plot_data_samples.PETPEESE       <- function(samples, x_seq, x_range, x_range_quant, n_points, transformation, transformation_arguments, transformation_settings){
 
   check_list(samples, "samples")
+
   if(is.null(samples[["PET"]]) & is.null(samples[["PEESE"]]))
     stop("At least one 'PET' or 'PEESE' model needs to be specified.")
   if(is.null(samples[["mu"]]))
@@ -1335,7 +1381,14 @@ plot_posterior <- function(samples, parameter, plot_type = "base", prior = FALSE
 .plot_data_samples.weightfunction <- function(samples, x_seq, x_range, x_range_quant, n_points){
 
   check_list(samples, "samples", check_names = "omega", allow_other = TRUE)
-  samples    <- samples[["omega"]]
+  if(!is.null(samples[["omega"]])){
+    samples <- samples[["omega"]]
+  }else if(!is.null(samples[["bias"]])){
+    samples <- samples[["bias"]]
+  }else{
+    stop("No 'omega' or 'bias' samples found.")
+  }
+
   prior_list <- attr(samples, "prior_list")
 
   # get the plotting range
diff --git a/R/model-averaging.R b/R/model-averaging.R
index 165978fb..1d62ae65 100644
--- a/R/model-averaging.R
+++ b/R/model-averaging.R
@@ -170,7 +170,7 @@ models_inference   <- function(model_list){
 #' @return \code{mix_posteriors} returns a named list of mixed posterior
 #' distributions (either a vector of matrix).
 #'
-#' @seealso [ensemble_inference] [BayesTools_ensemble_tables]
+#' @seealso [ensemble_inference] [BayesTools_ensemble_tables] [as_mixed_posteriors]
 #'
 #' @name mix_posteriors
 #' @export
@@ -289,6 +289,7 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F
 
   }
 
+  class(out) <- c(class(out), "mixed_posteriors")
   return(out)
 }
 
@@ -673,6 +674,478 @@ mix_posteriors <- function(model_list, parameters, is_null_list, conditional = F
   return(samples)
 }
 
+#' @title Export BayesTools JAGS model posterior distribution as model-average posterior distributions via \code{mix_posteriors}
+#'
+#' @description Creates a model-averages posterior distributions on a single
+#' model that allows mimicking the [mix_posteriors] functionality. This function
+#' is useful when the model-averaged ensemble is based on [prior_spike_and_slab]
+#' or [prior_mixture] priors - the model-averaging is done within the model.
+#'
+#' @param model model fit via the [JAGS_fit] function
+#' @param conditional a character vector of parameters to be conditioned on
+#' @param conditional_rule a character string specifying the rule for conditioning.
+#' Either "AND" or "OR". Defaults to "AND".
+#' @param force_plots temporal argument allowing to generate conditional posterior samples
+#' suitable for prior and posterior plots. Only available when conditioning on a
+#' single parameter.
+#' @inheritParams ensemble_inference
+#' @inheritParams mix_posteriors
+#'
+#' @return \code{as_mix_posteriors} returns a named list of mixed posterior
+#' distributions (either a vector of matrix).
+#'
+#' @seealso [mix_posteriors]
+#'
+#' @name as_mixed_posteriors
+#' @export
+as_mixed_posteriors <- function(model, parameters, conditional = NULL, conditional_rule = "AND", force_plots = FALSE){
+
+  # check input
+  if(!inherits(model, "BayesTools_fit"))
+    stop("'model' must be a 'BayesTools_fit'")
+  check_char(parameters, "parameters", check_length = FALSE)
+  check_char(conditional, "conditional", check_length = FALSE, allow_values = c(parameters, "PET", "PEESE", "PETPEESE", "omega"), allow_NULL = TRUE)
+  check_char(conditional_rule, "conditional_rule", allow_values = c("AND", "OR"))
+
+  # extract the list of priors
+  priors <- attr(model, "prior_list")
+
+  # extract the samples
+  model_samples <- suppressWarnings(coda::as.mcmc(model))
+  if(!is.matrix(model_samples)){
+    # deal with automatic coercion into a vector in case of a single predictor
+    model_samples <- matrix(model_samples, ncol = 1)
+    colnames(model_samples) <- model$monitor
+  }
+
+  # apply conditioning
+  if(length(conditional) > 0){
+
+    # subset the posterior distribution
+    conditioning_samples <- do.call(cbind, lapply(conditional, function(parameter){
+
+      # special cases for PET / PEESE / PET-PEESE / weightfunctions
+      if(parameter == "PET" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_PET <- sapply(priors[["bias"]], is.prior.PET)
+        return(model_samples[, "bias_indicator"] %in% which(is_PET))
+      }
+      if(parameter == "PEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE)
+        return(model_samples[, "bias_indicator"] %in% which(is_PEESE))
+      }
+      if(parameter == "PETPEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_PET   <- sapply(priors[["bias"]], is.prior.PET)
+        is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE)
+        return(model_samples[, "bias_indicator"] %in% which(is_PET | is_PEESE))
+      }
+      if(parameter == "omega" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_weightfunction <- sapply(priors[["bias"]], is.prior.weightfunction)
+        return(model_samples[, "bias_indicator"] %in% which(is_weightfunction))
+      }
+
+      # normal cases
+      temp_prior <- priors[[parameter]]
+
+      if(is.prior.spike_and_slab(temp_prior)){
+
+        return(model_samples[,paste0(parameter, "_indicator")] == 1)
+
+      }else if(is.prior.mixture(temp_prior)){
+
+        components <- attr(temp_prior, "components")
+        if(!all(components %in% c("null", "alternative")))
+          stop("conditional mixture posterior distributions are available only for 'null' and 'alternative' components")
+
+        return(model_samples[,paste0(parameter, "_indicator")] %in% which(components == "alternative"))
+
+      }else{
+
+        warning(sprintf("The parameter '%s' is not a conditional parameter.", parameter), call. = FALSE, immediate. = TRUE)
+        return(rep(TRUE, nrow(model_samples)))
+
+      }
+    }))
+    conditioning_samples <- apply(conditioning_samples, 1, ifelse(conditional_rule == "AND", all, any))
+
+    if(sum(conditioning_samples) == 0){
+      warning("No samples left after conditioning.", call. = FALSE, immediate. = TRUE)
+      return(list())
+    }
+
+
+    model_samples <- model_samples[conditioning_samples,,drop=FALSE]
+
+    # set prior weights to 0 for null distributions
+    # TODO: this needs to be implemented for enabling of the conditional mixture posterior distributions when more than one components is present
+    # (e.g., conditional marginal and posterior plots)
+    # the current workaround is suitable only for a single parameters (to produce averaged prior and posterior plots)
+    if(length(conditional) == 1 && length(parameters) == 1 && conditional == parameters && force_plots){
+
+      # special cases for PET / PEESE / PET-PEESE / weightfunctions
+      if(conditional == "PET" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_PET <- sapply(priors[["bias"]], is.prior.PET)
+        for(i in seq(along = is_PET)){
+          if(!is_PET[i]){
+            priors[[parameters]][[i]][["prior_weights"]] <- 0
+          }
+        }
+      }else if(conditional == "PEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        for(i in seq(along = is_PEESE)){
+          if(!is_PEESE[i]){
+            priors[[parameters]][[i]][["prior_weights"]] <- 0
+          }
+        }
+      }else if(conditional == "PETPEESE" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_PET   <- sapply(priors[["bias"]], is.prior.PET)
+        is_PEESE <- sapply(priors[["bias"]], is.prior.PEESE)
+        for(i in seq(along = is_PET)){
+          if(!(is_PET[i] || is_PEESE[i])){
+            priors[[parameters]][[i]][["prior_weights"]] <- 0
+          }
+        }
+      }else if(conditional == "omega" && !is.null(priors[["bias"]]) && is.prior.mixture(priors[["bias"]])){
+        is_weightfunction <- sapply(priors[["bias"]], is.prior.weightfunction)
+        for(i in seq(along = is_weightfunction)){
+          if(!is_weightfunction[i]){
+            priors[[parameters]][[i]][["prior_weights"]] <- 0
+          }
+        }
+      }else if(is.prior.spike_and_slab(priors[[parameters]])){
+        priors[[parameters]][["inclusion"]] <- prior("spike", list(1))
+      }else if(is.prior.mixture(priors[[parameters]])){
+
+        components <- attr(priors[[parameters]], "components")
+
+        attr(priors[[parameters]], "prior_weights")[which(components == "null")] <- 0
+        for(i in seq(along = components)){
+          if(components[i] == "null"){
+            priors[[parameters]][[i]][["prior_weights"]] <- 0
+          }
+        }
+      }
+    }
+  }
+
+
+  out    <- list()
+
+  for(p in seq_along(parameters)){
+
+    # prepare parameter specific values
+    temp_parameter <- parameters[p]
+    temp_prior     <- priors[[temp_parameter]]
+
+    if(is.prior.spike_and_slab(temp_prior)){
+      # spike and slab priors
+      out[[temp_parameter]] <- .as_mixed_posteriors.spike_and_slab(model_samples, temp_prior, temp_parameter)
+
+    }else if(is.prior.mixture(temp_prior)){
+      # mixture priors
+      out[[temp_parameter]] <- .as_mixed_posteriors.mixture(model_samples, temp_prior, temp_parameter, conditional)
+
+    }else if(is.prior.weightfunction(temp_prior)){
+      # weight functions
+      out[[temp_parameter]] <- .as_mixed_posteriors.weightfunction(model_samples, temp_prior, temp_parameter)
+
+    }else if(is.prior.factor(temp_prior)){
+      # factor priors
+      out[[temp_parameter]] <- .as_mixed_posteriors.factor(model_samples, temp_prior, temp_parameter)
+
+    }else if(is.prior.vector(temp_prior)){
+      # vector priors
+      out[[temp_parameter]] <- .as_mixed_posteriors.vector(model_samples, temp_prior, temp_parameter)
+
+    }else if(is.prior.simple(temp_prior)){
+      # simple priors
+      out[[temp_parameter]] <- .as_mixed_posteriors.simple(model_samples, temp_prior, temp_parameter)
+
+    }else{
+      stop("The posterior samples cannot be mixed: unsupported prior distributions.")
+    }
+
+    # add formula relevant information
+    if(!is.null(attr(temp_prior, which = "parameter"))){
+      class(out[[temp_parameter]]) <- c(class(out[[temp_parameter]]), "mixed_posteriors.formula")
+      attr(out[[temp_parameter]], "formula_parameter")  <- attr(temp_prior, which = "parameter")
+    }
+
+    # add conditioning information
+    attr(out[[temp_parameter]], "conditional")      <- conditional
+    attr(out[[temp_parameter]], "conditional_rule") <- conditional_rule
+
+  }
+
+  attr(out, "prior_list")       <- priors
+  attr(out, "conditional")      <- conditional
+  attr(out, "conditional_rule") <- conditional_rule
+  class(out) <- c(class(out), "as_mixed_posteriors", "mixed_posteriors")
+  return(out)
+}
+
+.as_mixed_posteriors.simple         <- function(model_samples, prior, parameter){
+
+  # check input
+  check_char(parameter, "parameter", check_length = FALSE)
+
+  # gather information about the prior distribution
+  prior_info <- list(
+    "interaction"       = .is_prior_interaction(prior),
+    "interaction_terms" = attr(prior, "interaction_terms")
+  )
+
+  # prepare output objects
+  samples <- model_samples[, parameter]
+
+  # format the output
+  samples <- unname(samples)
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- FALSE
+  attr(samples, "parameter")  <- parameter
+  attr(samples, "prior_list") <- prior
+  attr(samples, "interaction")       <- if(length(prior_info) == 0) FALSE else prior_info[["interaction"]]
+  attr(samples, "interaction_terms") <- prior_info[["interaction_terms"]]
+  class(samples) <- c("mixed_posteriors", "mixed_posteriors.simple")
+
+  return(samples)
+}
+.as_mixed_posteriors.vector         <- function(model_samples, prior, parameter){
+
+  # check input
+  check_char(parameter, "parameter", check_length = FALSE)
+
+  # gather information about the prior distribution
+  K <- prior$parameter[["K"]]
+  if(length(K) != 1)
+    stop("all vector prior must be of the same length")
+
+  # prepare output objects
+  if(K == 1){
+    samples <- model_samples[, parameter, drop = FALSE]
+  }else{
+    samples <- model_samples[, paste0(parameter,"[",1:K,"]"), drop = FALSE]
+  }
+
+  rownames(samples) <- NULL
+  colnames(samples) <- paste0(parameter,"[",1:K,"]")
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- FALSE
+  attr(samples, "parameter")  <- parameter
+  attr(samples, "prior_list") <- prior
+  class(samples) <- c("mixed_posteriors", "mixed_posteriors.vector")
+
+  return(samples)
+}
+.as_mixed_posteriors.factor         <- function(model_samples, prior, parameter){
+
+  # check input
+  check_char(parameter, "parameter", check_length = FALSE)
+
+  # gather information about the prior distribution
+  prior_info <- list(
+    "levels"            = .get_prior_factor_levels(prior),
+    "level_names"       = .get_prior_factor_level_names(prior),
+    "interaction"       = .is_prior_interaction(prior),
+    "interaction_terms" = attr(prior, "interaction_terms"),
+    "treatment"         = is.prior.treatment(prior),
+    "independent"       = is.prior.independent(prior),
+    "orthonormal"       = is.prior.orthonormal(prior),
+    "meandif"           = is.prior.meandif(prior)
+  )
+
+
+  if(prior_info[["treatment"]]){
+
+    if(prior_info[["levels"]] == 1){
+
+      samples <- .as_mixed_posteriors.simple(model_samples, prior, parameter)
+      samples <- matrix(samples, ncol = 1)
+
+    }else{
+
+      samples <- lapply(1:prior_info[["levels"]], function(i) .as_mixed_posteriors.simple(model_samples, prior, paste0(parameter, "[", i, "]")))
+      samples <- do.call(cbind, samples)
+
+    }
+
+    rownames(samples) <- NULL
+    colnames(samples) <- paste0(parameter,"[",prior_info[["level_names"]][-1],"]")
+    attr(samples, "sample_ind") <- FALSE
+    attr(samples, "models_ind") <- FALSE
+    attr(samples, "parameter")  <- parameter
+    attr(samples, "prior_list") <- prior
+    class(samples) <- c("mixed_posteriors", "mixed_posteriors.factor", "mixed_posteriors.vector")
+
+  }else if(prior_info[["independent"]]){
+
+    if(prior_info[["levels"]] == 1){
+
+      samples <- .as_mixed_posteriors.simple(model_samples, prior, parameter)
+      samples <- matrix(samples, ncol = 1)
+
+    }else{
+
+      samples <- lapply(1:prior_info[["levels"]], function(i) .as_mixed_posteriors.simple(model_samples, prior, paste0(parameter, "[", i, "]")))
+      samples <- do.call(cbind, samples)
+
+    }
+
+    rownames(samples) <- NULL
+    colnames(samples) <- paste0(parameter,"[",prior_info[["level_names"]],"]")
+    attr(samples, "sample_ind") <- FALSE
+    attr(samples, "models_ind") <- FALSE
+    attr(samples, "parameter")  <- parameter
+    attr(samples, "prior_list") <- prior
+    class(samples) <- c("mixed_posteriors", "mixed_posteriors.factor", "mixed_posteriors.vector")
+
+  }else if(prior_info[["orthonormal"]] | prior_info[["meandif"]]){
+
+    prior$parameter[["K"]] <- prior_info[["levels"]]
+    samples <- .as_mixed_posteriors.vector(model_samples, prior, parameter)
+    class(samples) <- c(class(samples), "mixed_posteriors.factor")
+
+  }
+
+  attr(samples, "levels")            <- prior_info[["levels"]]
+  attr(samples, "level_names")       <- prior_info[["level_names"]]
+  attr(samples, "interaction")       <- if(length(prior_info) == 0) FALSE else prior_info[["interaction"]]
+  attr(samples, "interaction_terms") <- prior_info[["interaction_terms"]]
+  attr(samples, "treatment")         <- prior_info[["treatment"]]
+  attr(samples, "independent")       <- prior_info[["independent"]]
+  attr(samples, "orthonormal")       <- prior_info[["orthonormal"]]
+  attr(samples, "meandif")           <- prior_info[["meandif"]]
+
+  return(samples)
+}
+.as_mixed_posteriors.weightfunction <- function(model_samples, prior, parameter){
+
+  # check input
+  check_char(parameter, "parameter", check_length = FALSE)
+
+  # obtain mapping for the weight coefficients
+  omega_mapping <- weightfunctions_mapping(list(prior))
+  omega_cuts    <- weightfunctions_mapping(list(prior), cuts_only = TRUE)
+  omega_names   <- sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
+
+  # prepare output objects
+  samples <- model_samples[, sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",i,"]"))]
+
+  rownames(samples) <- NULL
+  colnames(samples) <- omega_names
+  attr(samples, "sample_ind") <- FALSE
+  attr(samples, "models_ind") <- FALSE
+  attr(samples, "parameter")  <- parameter
+  attr(samples, "prior_list") <- prior
+  class(samples) <- c("mixed_posteriors", "mixed_posteriors.weightfunction")
+
+  return(samples)
+}
+.as_mixed_posteriors.spike_and_slab <- function(model_samples, prior, parameter){
+
+  # check input
+  check_char(parameter, "parameter", check_length = FALSE)
+
+  prior_variable <- prior[["variable"]]
+
+  # prepare output objects
+  if(is.prior.factor(prior_variable)){
+
+    samples <- .as_mixed_posteriors.factor(model_samples, prior_variable, parameter)
+    attr(samples, "models_ind") <- as.vector(model_samples[,paste0(parameter, "_indicator")])
+
+  }else if(is.prior.simple(prior_variable)){
+
+    samples <- .as_mixed_posteriors.simple(model_samples, prior_variable, parameter)
+    attr(samples, "models_ind") <- as.vector(model_samples[,paste0(parameter, "_indicator")])
+
+  }
+
+  class(samples) <- c("mixed_posteriors.spike_and_slab", class(samples))
+  attr(samples, "prior_list") <- prior
+
+  return(samples)
+}
+.as_mixed_posteriors.mixture        <- function(model_samples, prior, parameter, conditional){
+
+  # check input
+  check_char(parameter, "parameter", check_length = FALSE)
+
+
+  # prepare output objects
+  if(inherits(prior, "prior.bias_mixture")){
+
+    is_PET            <- sapply(prior, is.prior.PET)
+    is_PEESE          <- sapply(prior, is.prior.PEESE)
+    is_weightfunction <- sapply(prior, is.prior.weightfunction)
+
+    # prepare weightfunction parameter names
+    if(any(is_weightfunction)){
+      omega_mapping <- weightfunctions_mapping(prior[is_weightfunction], one_sided = TRUE)
+      omega_cuts    <- weightfunctions_mapping(prior[is_weightfunction], cuts_only = TRUE, one_sided = TRUE)
+      omega_names   <- sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
+      omega_par     <- rev(sapply(1:(length(omega_cuts)-1), function(i) paste0("omega[",i,"]")))
+    }
+
+    # deal with conditional parameters
+    if(length(conditional) > 0 && any(c("PET", "PEESE", "PETPEESE", "omega") %in% conditional)){
+
+      if("omega" %in% conditional){
+        out_names <- omega_names
+        par_names <- omega_par
+      }else if("PETPEESE" %in% conditional){
+        out_names <- par_names <- c("PET", "PEESE")
+      }else if("PET" %in% conditional){
+        out_names <- par_names <- "PET"
+      }else if("PEESE" %in% conditional){
+        out_names <- par_names <- "PEESE"
+      }
+
+    }else{
+
+      out_names <- NULL
+      par_names <- NULL
+
+      if(any(is_weightfunction)){
+        out_names <- c(out_names, omega_names)
+        par_names <- c(par_names, omega_par)
+      }
+      if(any(is_PET)){
+        out_names <- c(out_names, "PET")
+        par_names <- c(par_names, "PET")
+      }
+      if(any(is_PEESE)){
+        out_names <- c(out_names, "PEESE")
+        par_names <- c(par_names, "PEESE")
+      }
+    }
+
+    # select samples
+    samples   <- model_samples[, par_names,drop=FALSE]
+    indicator <- model_samples[,paste0(parameter, "_indicator")]
+
+    rownames(samples) <- NULL
+    colnames(samples) <- out_names
+    attr(samples, "sample_ind") <- FALSE
+    attr(samples, "models_ind") <- as.vector(indicator)
+    attr(samples, "parameter")  <- parameter
+    attr(samples, "prior_list") <- prior
+    class(samples) <- c("mixed_posteriors", "mixed_posteriors.bias")
+
+  }else{
+
+    if(inherits(prior, "prior.simple_mixture")){
+      samples <- .as_mixed_posteriors.simple(model_samples, prior, parameter)
+    }else if(inherits(prior, "prior.factor_mixture")){
+      samples <- .as_mixed_posteriors.factor(model_samples, prior, parameter)
+    }
+    attr(samples, "models_ind") <- as.vector(model_samples[,paste0(parameter, "_indicator")])
+
+  }
+
+  class(samples) <- c("mixed_posteriors.mixture", class(samples))
+  attr(samples, "prior_list") <- prior
+
+  return(samples)
+}
 
 #' @title Compute inclusion Bayes factors
 #'
@@ -759,19 +1232,20 @@ inclusion_BF         <- function(prior_probs, post_probs, margliks, is_null){
 #'
 #' @param prior_list list of prior distributions
 #' @param cuts_only whether only p-value cuts should be returned
+#' @param one_sided force one-sided output
 #'
 #' @return \code{weightfunctions_mapping} returns a list of indices
 #' mapping the publication weights omega from the individual weightfunctions
 #' into a joint weightfunction.
 #'
 #' @export
-weightfunctions_mapping <- function(prior_list, cuts_only = FALSE){
+weightfunctions_mapping <- function(prior_list, cuts_only = FALSE, one_sided = FALSE){
 
   # check input
   if(!all(sapply(prior_list, is.prior.weightfunction) | sapply(prior_list, is.prior.point) | sapply(prior_list, is.prior.none)))
     stop("'priors' must be a list of weightfunction priors distributions")
   check_bool(cuts_only, "cuts_only")
-
+  check_bool(one_sided, "one_sided")
 
   # extract cuts and types
   priors_cuts <- lapply(prior_list, function(prior)rev(prior[["parameters"]][["steps"]]))
@@ -780,7 +1254,7 @@ weightfunctions_mapping <- function(prior_list, cuts_only = FALSE){
 
   # get new cutpoint appropriate cut-points
   priors_cuts_new <- priors_cuts
-  if(any(grepl("one.sided", priors_type))){
+  if(one_sided || any(grepl("one.sided", priors_type))){
 
     # translate two.sided into one.sided
     for(p in seq_along(priors_type)){
@@ -809,7 +1283,7 @@ weightfunctions_mapping <- function(prior_list, cuts_only = FALSE){
         l = c(0, priors_cuts_new[[p]]),
         u = c(priors_cuts_new[[p]], 1))
 
-      if(any(grepl("one.sided", priors_type))){
+      if(one_sided || any(grepl("one.sided", priors_type))){
         if(grepl("two.sided", priors_type[p])){
           omega_ind[[p]] <- rev(c( (length(priors_cuts[[p]]) + 1):2, 1:(length(priors_cuts[[p]]) + 1) ))
         }else if(grepl("one.sided", priors_type[p])){
diff --git a/R/priors-plot.R b/R/priors-plot.R
index 895cf0c6..109d6971 100644
--- a/R/priors-plot.R
+++ b/R/priors-plot.R
@@ -55,6 +55,14 @@ plot.prior <- function(x, plot_type = "base",
   check_bool(rescale_x, "rescale_x")
   check_int(show_figures, "show_figures", allow_NULL = TRUE)
 
+  if(is.prior.mixture(x)){
+    class(x) <- NULL
+    return(plot_prior_list(x, plot_type = plot_type, x_seq = x_seq, xlim = xlim, x_range_quant = x_range_quant, n_points = n_points,
+                           n_samples = n_samples, force_samples = force_samples, transformation = transformation,
+                           transformation_arguments = transformation_arguments, transformation_settings = transformation_settings,
+                           show_figures = show_figures, individual = individual, rescale_x = rescale_x, par_name = par_name, ...))
+  }
+
 
   # get the plotting data
   if(is.null(xlim) & is.null(x_seq)){
@@ -732,6 +740,15 @@ lines.prior <- function(x, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_po
   check_int(show_parameter, "show_parameter", allow_NULL = TRUE)
   check_real(scale_y2, "scale_y2", lower = 0)
 
+  if(is.prior.mixture(x)){
+    class(x) <- NULL
+    return(lines_prior_list(x, xlim = xlim, x_seq = x_seq, x_range_quant = x_range_quant, n_points = n_points,
+                            n_samples = n_samples, force_samples = force_samples,
+                            transformation = transformation, transformation_arguments = transformation_arguments, transformation_settings = transformation_settings,
+                            rescale_x = rescale_x, scale_y2 = scale_y2, ...))
+  }
+
+
 
   # get the plotting data
   if(is.null(xlim) & is.null(x_seq)){
@@ -776,6 +793,12 @@ lines.prior <- function(x, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_po
     return(invisible())
   }
 
+  # plot spike and slab prior
+  if(is.prior.spike_and_slab(x)){
+    .lines.prior.spike_and_slab(plot_data, ...)
+    return(invisible())
+  }
+
   # point prior plots
   if(is.prior.point(x)){
     .lines.prior.point(plot_data, scale_y2 = scale_y2, ...)
@@ -794,12 +817,6 @@ lines.prior <- function(x, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_po
     return(invisible())
   }
 
-  # plot spike and slab prior
-  if(is.prior.spike_and_slab(x)){
-    .lines.prior.spike_and_slab(plot_data, ...)
-    return(invisible())
-  }
-
   # default prior plots
   if(is.prior.simple(x)){
     .lines.prior.simple(plot_data, ...)
@@ -831,6 +848,13 @@ geom_prior  <- function(x, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_po
   check_int(show_parameter, "show_parameter", allow_NULL = TRUE)
   check_real(scale_y2, "scale_y2", lower = 0)
 
+  if(is.prior.mixture(x)){
+    class(x) <- NULL
+    return(geom_prior_list(x, xlim = xlim, x_seq = x_seq, x_range_quant = x_range_quant, n_points = n_points,
+                            n_samples = n_samples, force_samples = force_samples,
+                            transformation = transformation, transformation_arguments = transformation_arguments, transformation_settings = transformation_settings,
+                            rescale_x = rescale_x, scale_y2 = scale_y2, ...))
+  }
 
   # get the plotting data
   if(is.null(xlim) & is.null(x_seq)){
@@ -873,6 +897,13 @@ geom_prior  <- function(x, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_po
   }
 
 
+  # plot spike and slab prior
+  if(is.prior.spike_and_slab(x)){
+    geom <- .geom_prior.spike_and_slab(plot_data, ...)
+    return(geom)
+  }
+
+
   # plot point prior
   if(is.prior.point(x)){
     geom <- .geom_prior.point(plot_data, ...)
@@ -894,13 +925,6 @@ geom_prior  <- function(x, xlim = NULL, x_seq = NULL, x_range_quant = NULL, n_po
   }
 
 
-  # plot spike and slab prior
-  if(is.prior.spike_and_slab(x)){
-    geom <- .geom_prior.spike_and_slab(plot_data, ...)
-    return(geom)
-  }
-
-
   # default prior plots
   if(is.prior.simple(x)){
     geom <- .geom_prior.simple(plot_data, ...)
diff --git a/R/priors-print.R b/R/priors-print.R
index dfc14800..dd3706d0 100644
--- a/R/priors-print.R
+++ b/R/priors-print.R
@@ -49,6 +49,13 @@ print.prior <- function(x, short_name = FALSE, parameter_names = FALSE, plot = F
     silent <- TRUE
   }
 
+  dots <- list(...)
+  if(is.null(dots[["inline"]])){
+    inline <- FALSE
+  }else{
+    inline <- dots[["inline"]]
+    check_bool(inline, "inline")
+  }
 
   if(is.prior.none(x)){
     output <- .print.prior.none(x, short_name, parameter_names, plot, digits_estimates, silent)
@@ -58,6 +65,8 @@ print.prior <- function(x, short_name = FALSE, parameter_names = FALSE, plot = F
     output <- .print.prior.weightfunction(x, short_name, parameter_names, plot, digits_estimates, silent)
   }else if(is.prior.spike_and_slab(x)){
     output <- .print.prior.spike_and_slab(x, short_name, parameter_names, plot, digits_estimates, silent)
+  }else if(is.prior.mixture(x)){
+    output <- .print.prior.mixture(x, short_name, parameter_names, plot, digits_estimates, silent, inline)
   }
 
 
@@ -321,3 +330,40 @@ print.prior <- function(x, short_name = FALSE, parameter_names = FALSE, plot = F
 
   return(output)
 }
+.print.prior.mixture        <- function(x, short_name, parameter_names, plot, digits_estimates, silent, inline){
+
+  prior_names <- lapply(x, function(p){
+    print(p,  short_name, parameter_names, plot, digits_estimates, silent = TRUE)
+  })
+
+  prior_weights <- attr(x, "prior_weights")
+  prior_weights <- paste0("(", round(prior_weights, digits_estimates), "/", round(sum(prior_weights), digits_estimates), ")")
+
+  prior_components <- attr(x, "components")
+  if(all(prior_components %in% c("null", "alternative"))){
+    prior_components <- sort(prior_components)
+  }
+
+  if(!plot){
+
+    output <- NULL
+
+    # inline printing for summary tables
+    if(inline){
+      output <- paste0(paste0(prior_weights, " * ", prior_names), collapse = " + ")
+    }else{
+      for(component in unique(prior_components)){
+        output <- paste0(output, component, ":\n")
+        for(i in seq_along(prior_components)[prior_components == component]){
+          output <- paste0(output, "  ", prior_weights[i], " * ", prior_names[[i]], "\n")
+        }
+      }
+    }
+
+  }else{
+    output <- Map(function(weight, prior) bquote(.(as.name(weight))~"*"~.(prior)), prior_weights, prior_names)
+    output <- Reduce(function(x, y) bquote(.(x)~+~.(y)), output)
+  }
+
+  return(output)
+}
diff --git a/R/priors-tools.R b/R/priors-tools.R
index e71bb7ca..045a549b 100644
--- a/R/priors-tools.R
+++ b/R/priors-tools.R
@@ -196,6 +196,41 @@
     return(attr(prior, "level_names"))
   }
 }
+.get_prior_factor_list_type   <- function(prior_list){
+
+  priors_type <- do.call(rbind, lapply(prior_list, function(p) {
+    if(is.prior.point(p) | is.prior.none(p)){
+      return(data.frame(
+        "treatment"         = NA,
+        "independent"       = NA,
+        "orthonormal"       = NA,
+        "meandif"           = NA,
+        "K"                 = NA
+      ))
+    }else{
+      return(data.frame(
+        "treatment"         = is.prior.treatment(p),
+        "independent"       = is.prior.independent(p),
+        "orthonormal"       = is.prior.orthonormal(p),
+        "meandif"           = is.prior.meandif(p),
+        "K"                 = if(!is.null(p[["parameters"]][["K"]])) p[["parameters"]][["K"]] else NA
+      ))
+    }
+  }))
+  priors_type <- priors_type[!apply(priors_type, 1, function(x) all(is.na(x))),]
+  priors_type <- unique(priors_type)
+
+  if(nrow(priors_type) > 1)
+    stop("The prior_list must contain only one type of factor priors.")
+
+  class_type <- unlist(priors_type[-ncol(priors_type)])
+  class_type <- names(class_type)[which(class_type)]
+
+  return(list(
+    "class" = paste0("prior.", class_type),
+    "K"     = priors_type[["K"]]
+  ))
+}
 .is_prior_interaction         <- function(prior){
   if(is.null(attr(prior, "interaction"))){
     return(FALSE)
@@ -244,6 +279,7 @@
 #' @export is.prior.treatment
 #' @export is.prior.independent
 #' @export is.prior.spike_and_slab
+#' @export is.prior.mixture
 #' @name is.prior
 NULL
 
@@ -307,10 +343,60 @@ is.prior.spike_and_slab  <- function(x){
 is.prior.meandif         <- function(x){
   inherits(x, "prior.meandif")
 }
+#' @rdname is.prior
+is.prior.mixture         <- function(x){
+  inherits(x, "prior.mixture")
+}
 
 .check_prior <- function(prior, name = "prior"){
+
   if(!is.prior(prior))
-    stop(paste0("The '", name, "' argument must be a valid prior object."))
+    stop(paste0("The '", name, "' argument must be a valid prior object."), call. = FALSE)
+
+  return()
+}
+.check_prior_list <- function(prior_list, name = "prior_list", check_length = 0, allow_NULL = FALSE,
+                              allow_prior.point = TRUE, allow_prior.simple = TRUE, allow_prior.discrete = TRUE, allow_prior.vector = TRUE,
+                              allow_prior.PET = TRUE, allow_prior.PEESE = TRUE, allow_prior.weightfunction = TRUE, allow_prior.factor = TRUE){
+
+  check_list(prior_list, name, check_length = check_length, allow_other = FALSE, allow_NULL = allow_NULL)
+
+  if(allow_NULL && is.null(prior_list) || length(prior_list) == 0)
+    return()
+
+  for(i in seq_along(prior_list)){
+    .check_prior(prior_list[[i]], paste0(name, "[[", i, "]] argument must be a prior distribution."))
+
+    if(!allow_prior.point && is.prior.point(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain point priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.simple && is.prior.simple(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain simple priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.discrete && is.prior.discrete(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain discrete priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.vector && is.prior.vector(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain vector priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.PET && is.prior.PET(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain PET priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.PEESE && is.prior.PEESE(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain PEESE priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.weightfunction && is.prior.weightfunction(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain weightfunction priors (element [[", i, "]])."), call. = FALSE)
+
+    if(!allow_prior.factor && is.prior.factor(prior_list[[i]]))
+      stop(paste0("The '", name, "' argument must not contain factor priors (element [[", i, "]])."), call. = FALSE)
+
+    if(is.prior.mixture(prior_list[[i]])){
+      stop(paste0("The '", name, "' argument must not contain mixture priors (element [[", i, "]])."), call. = FALSE)
+    }
+  }
+
+  return()
 }
 
 
diff --git a/R/priors.R b/R/priors.R
index c7521bc6..1e84fcb3 100644
--- a/R/priors.R
+++ b/R/priors.R
@@ -169,6 +169,14 @@ prior_none <- function(prior_weights = 1){
 #' parameters, and sets the model priors odds to the product of
 #' its prior distributions.
 #'
+#' @details Constrained cases of weight functions can be specified by adding
+#' ".fixed" after the distribution name, i.e., \code{"two.sided.fixed"} and
+#' \code{"one.sided.fixed"}. In these cases, the functions are specified using
+#' \code{steps} and \code{omega} parameters, where the \code{omega} parameter
+#' is a vector of weights that corresponds to the relative publication probability
+#' (i.e., no parameters are estimated).
+#'
+#'
 #' @examples
 #' p1 <- prior_weightfunction("one-sided", parameters = list(steps = c(.05, .10), alpha = c(1, 1, 1)))
 #'
@@ -404,12 +412,123 @@ prior_spike_and_slab <- function(prior_parameter,
     inclusion = prior_inclusion
   )
 
-  class(output) <- c("prior", "prior.spike_and_slab")
+  # distinguish normal and factor prior distributions
+  if(is.prior.factor(prior_parameter)){
+
+    # obtain and store the contrast type
+    priors_type <- .get_prior_factor_list_type(list(prior_parameter))
+
+    attr(prior_parameter, "K") <- priors_type[["K"]]
+    class(output)              <- c("prior", "prior.spike_and_slab", "prior.factor_spike_and_slab", priors_type[["class"]])
+
+  }else if(is.prior.simple(prior_parameter)){
+    class(output) <- c("prior", "prior.spike_and_slab", "prior.simple_spike_and_slab")
+  }else{
+    stop("The 'prior_parameter' must be either a simple or factor prior distribution.")
+  }
 
   return(output)
 }
 
 
+#' @title Creates a mixture of prior distributions
+#' @description \code{prior_mixture} creates a mixture of prior distributions.
+#' This is a more generic version of the \code{prior_spike_and_slab} function.
+#'
+#' @param prior_list a list of prior distributions to be mixed.
+#' @param is_null a logical vector indicating which of the prior distributions
+#' should be considered as a null distribution. Defaults to \code{rep(FALSE, length(prior_list))}.
+#' @param components a character vector indicating which of the prior distributions
+#' belong to the same mixture component (this is an alternative specification to the \code{is_null} argument).
+#' Defaults to \code{NULL} (i.e., \code{is_null} is used.
+#'
+#' @seealso [prior()]
+#' @export
+prior_mixture <- function(prior_list, is_null = rep(FALSE, length(prior_list)), components = NULL){
+
+  .check_prior_list(prior_list)
+  check_bool(is_null, "is_null",       check_length = length(prior_list), allow_NULL = TRUE)
+  check_char(components, "components", check_length = length(prior_list), allow_NULL = TRUE)
+  if(is.null(is_null) && is.null(components))
+    stop("Either 'is_null' or 'components' must be specified.")
+
+  if(is.null(components)){
+    components <- ifelse(is_null, "null", "alternative")
+  }
+
+  for(i in seq_along(prior_list)){
+    attr(prior_list[[i]], "component") <- components[i]
+  }
+
+
+  # distinguish normal, factor, and publication bias mixture priors
+  if(any(sapply(prior_list, is.prior.factor))){
+
+    # test that the prior is either a factor prior or a spike prior
+    if(!all(sapply(prior_list, is.prior.factor) | sapply(prior_list, is.prior.point) | sapply(prior_list, is.prior.none)))
+      stop("Factor prior mixture requires that all priors are either factor priors or spike prior distributions")
+
+    # obtain and store the contrast type
+    priors_type <- .get_prior_factor_list_type(prior_list)
+
+    # change prior none/spikes into factor prior spikes
+    for(i in seq_along(prior_list)){
+      if(is.prior.point(prior_list[[i]])){
+        prior_list[[i]] <- prior_factor(
+          distribution = "point",
+          parameters   = list(location = prior_list[[i]][["parameters"]][["location"]]),
+          contrast     = gsub("prior.", "", priors_type[["class"]], fixed = TRUE)
+        )
+      }else if(is.prior.none(prior_list[[i]])){
+        prior_list[[i]] <- prior_factor(
+          distribution = "point",
+          parameters   = list(location = 0),
+          contrast     = gsub("prior.", "", priors_type[["class"]], fixed = TRUE)
+        )
+      }
+    }
+
+    attr(prior_list, "K")  <- priors_type[["K"]]
+    class(prior_list)      <- c("prior", "prior.factor_mixture", "prior.mixture", priors_type[["class"]])
+
+
+  }else if(any(sapply(prior_list, is.prior.PET)) || any(sapply(prior_list, is.prior.PEESE)) || any(sapply(prior_list, is.prior.weightfunction))){
+
+    # test that the prior is either a PET, PEESE, or weightfunction prior
+    if(!all(sapply(prior_list, is.prior.PET) | sapply(prior_list, is.prior.PEESE) | sapply(prior_list, is.prior.weightfunction) | sapply(prior_list, is.prior.none)))
+      stop("PET/PEESE/weightfunction prior mixture requires that all priors are either PET, PEESE, or weightfunction prior distributions")
+
+    class(prior_list) <- c("prior", "prior.bias_mixture", "prior.mixture")
+
+
+  }else if(any(sapply(prior_list, is.prior.simple))){
+
+    # test that all priors are simple priors
+    if(!all(sapply(prior_list, is.prior.simple) | sapply(prior_list, is.prior.none)))
+      stop("Simple prior mixture requires that all priors are simple prior distributions")
+
+    # change none into prior spikes
+    for(i in seq_along(prior_list)){
+      if(is.prior.none(prior_list[[i]])){
+        prior_list[[i]] <- prior(
+          distribution = "point",
+          parameters   = list(location = 0)
+        )
+      }
+    }
+
+    class(prior_list) <- c("prior", "prior.simple_mixture", "prior.mixture")
+
+  }else{
+    stop("The prior mixture must contain either factors, publication bias components, or simple prior distributions.")
+  }
+
+  attr(prior_list, "components")    <- components
+  attr(prior_list, "prior_weights") <- sapply(prior_list, function(p) p[["prior_weights"]])
+
+  return(prior_list)
+}
+
 #### functions for constructing prior distributions ####
 .prior_normal    <- function(parameters, truncation){
 
@@ -857,7 +976,7 @@ prior_spike_and_slab <- function(prior_parameter,
   output <- list()
 
   # check overall settings
-  parameters <- .check_and_name_parameters(parameters, c("steps", "omega"), "two-sided.fixed weightfunction")
+  parameters <- .check_and_name_parameters(parameters, c("steps", "omega"), "one-sided.fixed weightfunction")
 
   # check individual parameters
   .check_parameter_weigthfunction(parameters$steps, omega = parameters$omega)
@@ -944,8 +1063,60 @@ rng.prior   <- function(x, n, ...){
   }else{
     transform_factor_samples <- TRUE
   }
+  if(!is.null(dots[["sample_components"]])){
+    check_bool(dots[["sample_components"]], "sample_components")
+    sample_components <- dots[["sample_components"]]
+  }else{
+    sample_components <- FALSE
+  }
+
+  if(is.prior.spike_and_slab(prior)){
+
+    inclusion <- stats::rbinom(n, size = 1, prob = rng(prior[["inclusion"]], n))
+
+    if(sample_components)
+      return(inclusion)
+
+    x         <- rng(prior[["variable"]], n) * inclusion
+    attr(x, "inclusion") <- inclusion
+
+  }else if(is.prior.mixture(prior)){
+
+    component_probabilities <- attr(prior, "prior_weights")
+    components              <- sample(seq_along(component_probabilities), size = n, replace = TRUE, prob = component_probabilities)
+
+    if(sample_components)
+      return(components)
+
+    if(inherits(prior, "prior.factor_mixture")){
+
+      prior_type <- .get_prior_factor_list_type(prior)
+
+      if(transform_factor_samples){
+        x <- matrix(NA, nrow = n, ncol = prior_type[["K"]] + 1)
+      }else{
+        x <- matrix(NA, nrow = n, ncol = prior_type[["K"]])
+      }
+
+      for(component in unique(components)){
+        x[component == components,] <- rng(prior[[component]], sum(component == components), transform_factor_samples = transform_factor_samples)
+      }
+
+    }else if(inherits(prior, "prior.simple_mixture")){
+
+      x <- rep(NA, n)
+      for(component in unique(components)){
+        x[component == components] <- rng(prior[[component]], sum(component == components))
+      }
+
+    }else{
+      stop("unsupported prior mixture type")
+    }
 
-  if(is.prior.simple(prior)){
+
+    attr(x, "components") <- components
+
+  }else if(is.prior.simple(prior)){
 
     x <- NULL
     # guesstimate the number of samples needed before the truncation
@@ -1064,10 +1235,6 @@ rng.prior   <- function(x, n, ...){
       "two.sided.fixed" = rtwo.sided_fixed(n, omega = prior$parameters[["omega"]])
     )
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    x <- rng(prior[["variable"]], n) * stats::rbinom(n, size = 1, prob = rng(prior[["inclusion"]], n))
-
   }
 
   return(x)
@@ -1080,7 +1247,15 @@ cdf.prior   <- function(x, q, ...){
   .check_q(q)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No cdfs are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No cdfs are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     p <- rep(NA, length(q))
 
@@ -1116,10 +1291,6 @@ cdf.prior   <- function(x, q, ...){
 
     stop("Only marginal cdfs are implemented for prior weightfunctions.")
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No cdfs are implemented for spike and slab priors.")
-
   }
 
   return(p)
@@ -1132,7 +1303,15 @@ ccdf.prior  <- function(x, q, ...){
   .check_q(q)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No ccdf are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No ccdf are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     p <- rep(NA, length(q))
 
@@ -1168,10 +1347,6 @@ ccdf.prior  <- function(x, q, ...){
 
     stop("Only marginal ccdf functions are implemented for prior weightfunctions.")
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No ccdf are implemented for spike and slab priors.")
-
   }
 
   return(p)
@@ -1185,7 +1360,15 @@ lpdf.prior  <- function(x, y, ...){
   .check_x(x)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No lpdf are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No lpdf are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     log_lik <- switch(
       prior[["distribution"]],
@@ -1237,10 +1420,6 @@ lpdf.prior  <- function(x, y, ...){
 
     stop("Only marginal lpdf are implemented for prior weightfunctions.")
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No lpdf are implemented for spike and slab priors.")
-
   }
 
   return(log_lik)
@@ -1267,7 +1446,15 @@ quant.prior <- function(x, p, ...){
   .check_p(p, log.p = FALSE)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No quantile functions are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No quantile functions are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     if(.is_prior_default_range(prior)){
 
@@ -1319,10 +1506,6 @@ quant.prior <- function(x, p, ...){
 
     stop("Only marginal quantile functions are implemented for prior weightfunctions.")
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No quantile functions are implemented for spike and slab priors.")
-
   }
 
   return(q)
@@ -1416,7 +1599,15 @@ mcdf.prior   <- function(x, q, ...){
   .check_q(q)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No mcdf are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No mcdf are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     p <- cdf(prior, q)
 
@@ -1475,10 +1666,6 @@ mcdf.prior   <- function(x, q, ...){
       "mpoint"  = ppoint(q, location = 0)
     )
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No cdf are implemented for spike and slab priors.")
-
   }
 
   return(p)
@@ -1491,7 +1678,15 @@ mccdf.prior  <- function(x, q, ...){
   .check_q(q)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No mccdf are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No mccdf are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     p <- ccdf(prior, q)
 
@@ -1550,10 +1745,6 @@ mccdf.prior  <- function(x, q, ...){
       "mpoint"  = ppoint(q, location = 0, lower.tail = FALSE),
     )
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No mccdf are implemented for spike and slab priors.")
-
   }
 
   return(p)
@@ -1567,7 +1758,15 @@ mlpdf.prior  <- function(x, y, ...){
   .check_x(x)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No mlpdf are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No mlpdf are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     log_lik <- lpdf(prior, x)
 
@@ -1626,10 +1825,6 @@ mlpdf.prior  <- function(x, y, ...){
       "mpoint"  = dpoint(x, location = 0, log = TRUE),
     )
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No lpdf are implemented for spike and slab priors.")
-
   }
 
   return(log_lik)
@@ -1656,7 +1851,15 @@ mquant.prior <- function(x, p, ...){
   .check_p(p, log.p = FALSE)
   .check_prior(prior)
 
-  if(is.prior.simple(prior)){
+  if(is.prior.spike_and_slab(prior)){
+
+    stop("No quantile functions are implemented for spike and slab priors.")
+
+  }else if(is.prior.mixture(prior)){
+
+    stop("No quantile functions are implemented for prior mixtures.")
+
+  }else if(is.prior.simple(prior)){
 
     q <- quant(prior, p)
 
@@ -1715,10 +1918,6 @@ mquant.prior <- function(x, p, ...){
       "mpoint"  = qpoint(p, location = 0)
     )
 
-  }else if(is.prior.spike_and_slab(prior)){
-
-    stop("No quantile functions are implemented for spike and slab priors.")
-
   }
 
   return(q)
@@ -1838,7 +2037,15 @@ mean.prior   <- function(x, ...){
 
   .check_prior(x, "x")
 
-  if(is.prior.simple(x)){
+  if(is.prior.spike_and_slab(x)){
+
+    m <- mean(x[["variable"]]) * mean(x[["inclusion"]])
+
+  }else if(is.prior.mixture(x)){
+
+    stop("No mean is implemented for prior mixtures.")
+
+  }else if(is.prior.simple(x)){
 
     if(.is_prior_default_range(x)){
 
@@ -1908,10 +2115,6 @@ mean.prior   <- function(x, ...){
       m <- 0
     }
 
-  }else if(is.prior.spike_and_slab(x)){
-
-    m <- mean(x[["variable"]]) * mean(x[["inclusion"]])
-
   }
 
   return(m)
@@ -1984,7 +2187,21 @@ var.prior   <- function(x, ...){
 
   .check_prior(x, "x")
 
-  if(is.prior.simple(x)){
+  if(is.prior.spike_and_slab(x)){
+
+    # the inclusion is always beta -> indicators are betabinom
+    var_inclusion <- with(x[["inclusion"]][["parameters"]], (alpha * beta * (alpha + beta + 1) ) / ( (alpha + beta)^2 * (alpha + beta + 1)  ) )
+
+    var <-
+      (var(x[["variable"]])  + mean(x[["variable"]])^2) *
+      (var_inclusion         + mean(x[["inclusion"]])^2) -
+      (mean(x[["variable"]])^2 * mean(x[["inclusion"]])^2)
+
+  }else if(is.prior.mixture(x)){
+
+    stop("No var is implemented for prior mixtures.")
+
+  }else if(is.prior.simple(x)){
 
     if(.is_prior_default_range(x)){
 
@@ -2082,17 +2299,6 @@ var.prior   <- function(x, ...){
         "mt"      = var.prior(prior("t",      parameters = list(location = 0, scale = par2, df = x$parameters[["df"]]))))
     }
 
-  }else if(is.prior.spike_and_slab(x)){
-
-    # the inclusion is always beta -> indicators are betabinom
-    var_inclusion <- with(x[["inclusion"]][["parameters"]], (alpha * beta * (alpha + beta + 1) ) / ( (alpha + beta)^2 * (alpha + beta + 1)  ) )
-
-
-    var <-
-      (var(x[["variable"]])  + mean(x[["variable"]])^2) *
-      (var_inclusion         + mean(x[["inclusion"]])^2) -
-      (mean(x[["variable"]])^2 * mean(x[["inclusion"]])^2)
-
   }
 
   return(var)
diff --git a/R/summary-tables.R b/R/summary-tables.R
index 9134977b..1e2f5af4 100644
--- a/R/summary-tables.R
+++ b/R/summary-tables.R
@@ -588,6 +588,8 @@ marginal_estimates_table <- function(samples, inference, parameters, probs = c(0
 #' should be excluded from the summary table. Defaults to \code{FALSE}.
 #' @param remove_parameters parameters to be removed from the summary. Defaults
 #' to \code{NULL}, i.e., including all parameters.
+#' @param return_samples whether to return the transoformed and formated samples
+#' instead of the table. Defaults to \code{FALSE}.
 #' @inheritParams BayesTools_ensemble_tables
 #'
 #'
@@ -615,7 +617,8 @@ marginal_estimates_table <- function(samples, inference, parameters, probs = c(0
 NULL
 
 #' @rdname BayesTools_model_tables
-model_summary_table <- function(model, model_description = NULL, title = NULL, footnotes = NULL, warnings = NULL, remove_spike_0 = TRUE, short_name = FALSE, formula_prefix = TRUE, remove_parameters = NULL){
+model_summary_table <- function(model, model_description = NULL, title = NULL, footnotes = NULL, warnings = NULL,
+                                remove_spike_0 = TRUE, short_name = FALSE, formula_prefix = TRUE, remove_parameters = NULL){
 
   # check input
   check_list(model, "model", check_names = "inference", allow_other = TRUE, all_objects = TRUE)
@@ -663,8 +666,8 @@ model_summary_table <- function(model, model_description = NULL, title = NULL, f
       next
     }else if(is.prior.weightfunction(prior_list[[i]]) | is.prior.PET(prior_list[[i]]) | is.prior.PEESE(prior_list[[i]])){
       temp_prior <- print(prior_list[[i]], silent = TRUE, short_name = short_name)
-    }else if(is.prior.simple(prior_list[[i]]) | is.prior.vector(prior_list[[i]]) | is.prior.factor(prior_list[[i]]) | is.prior.spike_and_slab(prior_list[[i]])){
-      temp_prior <- paste0(names(prior_list)[i], " ~ " , print(prior_list[[i]], silent = TRUE, short_name = short_name))
+    }else if(is.prior.simple(prior_list[[i]]) | is.prior.vector(prior_list[[i]]) | is.prior.factor(prior_list[[i]]) | is.prior.spike_and_slab(prior_list[[i]]) | is.prior.mixture(prior_list[[i]])){
+      temp_prior <- paste0(names(prior_list)[i], " ~ " , print(prior_list[[i]], silent = TRUE, short_name = short_name, inline = TRUE))
     }else if(is.prior.point(prior_list[[i]])){
       temp_prior <- paste0(names(prior_list)[i], " = " , print(prior_list[[i]], silent = TRUE, short_name = short_name))
     }
@@ -708,7 +711,9 @@ model_summary_table <- function(model, model_description = NULL, title = NULL, f
 }
 
 #' @rdname BayesTools_model_tables
-runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL, footnotes = NULL, warnings = NULL, conditional = FALSE, remove_spike_0 = TRUE, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE, remove_inclusion = FALSE, remove_parameters = NULL){
+runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL, footnotes = NULL, warnings = NULL, conditional = FALSE,
+                                     remove_spike_0 = TRUE, transform_factors = FALSE, transform_orthonormal = FALSE, formula_prefix = TRUE, remove_inclusion = FALSE, remove_parameters = NULL,
+                                     return_samples = FALSE){
 
   .check_runjags()
   # most of the code is shared with .diagnostics_plot_data function (keep them in sync on update)
@@ -738,64 +743,53 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
   # depreciate
   transform_factors <- .depreciate.transform_orthonormal(transform_orthonormal, transform_factors)
 
-  # obtain model information
-  invisible(utils::capture.output(runjags_summary <- suppressWarnings(summary(fit, silent.jags = TRUE))))
-  runjags_summary <- data.frame(runjags_summary)
-  model_samples   <- suppressWarnings(coda::as.mcmc(fit))
+  # get model samples
+  model_samples <- suppressWarnings(coda::as.mcmc(fit))
 
-  # change HPD to quantile intervals
-  for(par in rownames(runjags_summary)){
-    runjags_summary[par, "Lower95"] <- stats::quantile(model_samples[,par], .025, na.rm = TRUE)
-    runjags_summary[par, "Upper95"] <- stats::quantile(model_samples[,par], .975, na.rm = TRUE)
-  }
+  ### remove un-wanted estimates (or support values) - spike and slab priors already dealt with later (also remove the item from prior list)
+  for(i in rev(seq_along(prior_list))){
 
-  # deal with missing median in case of non-stochastic variables
-  if(!any(colnames(runjags_summary) == "Median")){
-    runjags_summary[,"Median"] <- NA
-  }
+    if(is.prior.simple(prior_list[[i]]) && prior_list[[i]][["distribution"]] == "invgamma"){
+      ## invgamma support parameter
+      model_samples <- model_samples[,colnames(model_samples) != paste0("inv_",names(prior_list)[i]),drop=FALSE]
+    }
 
-  # remove un-wanted estimates (or support values) - spike and slab priors already dealt with later
-  # also remove the item from prior list
-  for(i in rev(seq_along(prior_list))){
     if(is.prior.weightfunction(prior_list[[i]])){
+      ## simple weight functions
       # remove etas
       if(prior_list[[i]][["distribution"]] %in% c("one.sided", "two.sided")){
-        runjags_summary <- runjags_summary[!grepl("eta", rownames(runjags_summary)),,drop=FALSE]
+        model_samples <- model_samples[,!grepl("eta", colnames(model_samples)),drop=FALSE]
       }
-      # remove wrong diagnostics for the constant
-      runjags_summary[max(grep("omega", rownames(runjags_summary))),c("MCerr", "MC.ofSD","SSeff","psrf")] <- NA
-      # reorder
-      runjags_summary[grep("omega", rownames(runjags_summary)),] <- runjags_summary[rev(grep("omega", rownames(runjags_summary))),]
-      # rename
-      omega_cuts    <- weightfunctions_mapping(prior_list[i], cuts_only = TRUE)
-      omega_names   <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
-      rownames(runjags_summary)[grep("omega", rownames(runjags_summary))] <- omega_names
-      # remove if requested
+
+      # rename the omegas
+      omega_cuts      <- weightfunctions_mapping(prior_list[i], cuts_only = TRUE)
+      omega_names_old <- paste0("omega[", 1:(length(omega_cuts)-1),"]")
+      omega_names     <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
+
+      # change the order of omegas
+      model_samples[,which(colnames(model_samples) %in% omega_names_old)] <- model_samples[,rev(which(colnames(model_samples) %in% omega_names_old)),drop=FALSE]
+      colnames(model_samples)[which(colnames(model_samples) %in% omega_names_old)] <- omega_names
+
+      # remove omegas if requested
       if("omega" %in% remove_parameters){
-        prior_list[[i]] <- NULL
-        runjags_summary <- runjags_summary[,!rownames(runjags_summary) %in% omega_names]
+        model_samples   <- model_samples[,!colnames(model_samples) %in% omega_names,drop=FALSE]
+        prior_list[i] <- NULL
       }
+
     }else if((remove_spike_0 && is.prior.point(prior_list[[i]]) && prior_list[[i]][["parameters"]][["location"]] == 0) || (names(prior_list)[[i]] %in% remove_parameters)){
+      ## zero spike priors or other parameters to be removed
       if(is.prior.factor(prior_list[[i]])){
-        runjags_summary <- runjags_summary[!rownames(runjags_summary) %in% .JAGS_prior_factor_names(names(prior_list)[i], prior_list[[i]]),,drop=FALSE]
+        model_samples <- model_samples[,!colnames(model_samples) %in% .JAGS_prior_factor_names(names(prior_list)[i], prior_list[[i]]),drop=FALSE]
       }else{
-        runjags_summary <- runjags_summary[rownames(runjags_summary) != names(prior_list)[i],,drop=FALSE]
-      }
-      if(prior_list[[i]][["distribution"]] == "invgamma"){
-        runjags_summary <- runjags_summary[rownames(runjags_summary) != paste0("inv_",names(prior_list)[i]),,drop=FALSE]
+        model_samples <- model_samples[,colnames(model_samples) != names(prior_list)[i],drop=FALSE]
       }
       prior_list[i] <- NULL
-    }else if(is.prior.simple(prior_list[[i]]) && prior_list[[i]][["distribution"]] == "invgamma"){
-      runjags_summary <- runjags_summary[rownames(runjags_summary) != paste0("inv_",names(prior_list)[i]),,drop=FALSE]
     }
-  }
 
-  # remove transformations for removed variables
-  if(!is.null(transformations)){
-    transformations <-  transformations[names(transformations) %in% names(prior_list)]
   }
 
-  # simplify spike and slab priors to simple priors -- the samples and summary can be dealt with as any other prior
+  # simplify mixture and spike and slab priors to simple priors
+  # the samples and summary can be dealt with as any other prior (i.e., transformations later)
   for(par in names(prior_list)){
     if(is.prior.spike_and_slab(prior_list[[par]])){
 
@@ -813,41 +807,221 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
       # change the samples between conditional/averaged based on the preferences
       if(conditional){
 
-        # set the spike samples to NA
-        model_samples[
-          model_samples[,colnames(model_samples) == paste0(par, "_indicator")] == 0,
-          colnames(model_samples) %in% par_names] <- NA
-
-        # recompute the summaries
-        runjags_summary[par_names, "Mean"]    <- mean(model_samples[,par_names], na.rm = TRUE)
-        runjags_summary[par_names, "Median"]  <- stats::median(model_samples[,par_names], na.rm = TRUE)
-        runjags_summary[par_names, "SD"]      <- sd(model_samples[,par_names], na.rm = TRUE)
-        runjags_summary[par_names, "Lower95"] <- stats::quantile(model_samples[,par_names], .025, na.rm = TRUE)
-        runjags_summary[par_names, "Upper95"] <- stats::quantile(model_samples[,par_names], .975, na.rm = TRUE)
+        # compute the number of conditional samples
+        n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] == 1)
+
+        # replace null samples with NAs (important for later transformations)
+        model_samples[model_samples[,colnames(model_samples) == paste0(par, "_indicator")] != 1, par_names] <- NA
+
+        # add warnings about conditional summary
+        warnings <- c(warnings, .runjags_conditional_warning(par_names, n_conditional_samples))
       }
 
-      # remove the indicator
-      runjags_summary <- runjags_summary[rownames(runjags_summary) != paste0(par, "_indicator"),,drop=FALSE]
-      model_samples   <- model_samples[colnames(runjags_summary) != paste0(par, "_indicator"),,drop=FALSE]
+      # remove the inclusion
+      model_samples   <- model_samples[,colnames(model_samples) != paste0(par, "_inclusion"),drop=FALSE]
 
       # remove the latent variable
-      runjags_summary <- runjags_summary[!rownames(runjags_summary) %in% gsub(par, paste0(par, "_variable"), par_names),,drop=FALSE]
-      model_samples   <- model_samples[!colnames(runjags_summary) %in% gsub(par, paste0(par, "_variable"), par_names),,drop=FALSE]
+      model_samples   <- model_samples[,!colnames(model_samples) %in% gsub(par, paste0(par, "_variable"), par_names),drop=FALSE]
 
       # remove/rename the inclusions probabilities
       if(remove_inclusion){
-        runjags_summary <- runjags_summary[rownames(runjags_summary) != paste0(par, "_inclusion"),,drop=FALSE]
-        model_samples   <- model_samples[colnames(runjags_summary) != paste0(par, "_inclusion"),,drop=FALSE]
+        model_samples   <- model_samples[,colnames(model_samples) != paste0(par, "_indicator"),drop=FALSE]
       }else{
-        rownames(runjags_summary)[rownames(runjags_summary) == paste0(par, "_inclusion")] <- paste0(par, " (inclusion)")
-        colnames(model_samples)[colnames(model_samples) == paste0(par, "_inclusion")] <- paste0(par, " (inclusion)")
+        colnames(model_samples)[colnames(model_samples) == paste0(par, "_indicator")] <- paste0(par, " (inclusion)")
       }
 
       # modify the parameter list
       prior_list[[par]] <- prior_list[[par]]$variable
+
+    }else if(is.prior.mixture(prior_list[[par]])){
+
+      # check for publication bias component
+      is_PET            <- sapply(prior_list[[par]], is.prior.PET)
+      is_PEESE          <- sapply(prior_list[[par]], is.prior.PEESE)
+      is_weightfunction <- sapply(prior_list[[par]], is.prior.weightfunction)
+
+      # distinguish between null/alternative and component type notations
+      components <- attr(prior_list[[par]], "components")
+
+      if(any(is_PET | is_PEESE | is_weightfunction)){
+
+        # change the samples between conditional/averaged based on the preferences
+        if(conditional){
+
+          if(any(is_PET)){
+            # compute the number of conditional samples
+            n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(is_PET))
+
+            # replace null samples with NAs (important for later transformations)
+            model_samples[!model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(is_PET), "PET"] <- NA
+
+            # add warnings about conditional summary
+            warnings <- c(warnings, .runjags_conditional_warning("PET", n_conditional_samples))
+          }
+
+          if(any(is_PEESE)){
+            # compute the number of conditional samples
+            n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(is_PEESE))
+
+            # replace null samples with NAs (important for later transformations)
+            model_samples[!model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(is_PEESE), "PEESE"] <- NA
+
+            # add warnings about conditional summary
+            warnings <- c(warnings, .runjags_conditional_warning("PEESE", n_conditional_samples))
+          }
+
+          if(any(is_weightfunction)){
+            # compute the number of conditional samples
+            n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(is_weightfunction))
+
+            # replace null samples with NAs (important for later transformations)
+            model_samples[!model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(is_weightfunction), grepl("omega", colnames(model_samples))] <- NA
+
+            # add warnings about conditional summary
+            warnings <- c(warnings, .runjags_conditional_warning("omega", n_conditional_samples))
+          }
+
+        }
+
+        # re-format the weightfunctions
+        if(any(is_weightfunction)){
+
+          # reorder
+          model_samples[,grep("omega", colnames(model_samples))] <- model_samples[,rev(grep("omega", colnames(model_samples))),drop=FALSE]
+
+          # rename (the order does not need to be changed since the mixture combination returs oposite order to single priors)
+          omega_cuts      <- weightfunctions_mapping(prior_list[[par]][is_weightfunction], cuts_only = TRUE, one_sided = TRUE)
+          omega_names_old <- paste0("omega[", 1:(length(omega_cuts)-1),"]")
+          omega_names     <- sapply(1:(length(omega_cuts)-1), function(i)paste0("omega[",omega_cuts[i],",",omega_cuts[i+1],"]"))
+          colnames(model_samples)[which(colnames(model_samples) %in% omega_names_old)] <- omega_names
+
+          # remove if requested
+          if("omega" %in% remove_parameters){
+            model_samples <- model_samples[,!colnames(model_samples) %in% omega_names,drop=FALSE]
+            prior_list[[par]][is_weightfunction] <- NULL
+          }
+        }
+
+        # add the simpler priors to the prior list
+        if(any(is_PET)){
+          prior_list[["PET"]] <- prior_list[[par]][is_PET][1]
+        }
+        if(any(is_PEESE)){
+          prior_list[["PEESE"]] <- prior_list[[par]][is_PEESE][1]
+        }
+        if(any(is_weightfunction)){
+          prior_list[["omega"]] <- prior_list[[par]][is_weightfunction][1]
+        }
+
+      }else{
+
+        # prepare parameter names
+        par_names <- par
+
+        # change the samples between conditional/averaged based on the preferences
+        if(conditional){
+
+          if(all(components %in% c("null", "alternative"))){
+
+            # select the corresponding indicators
+            this_component_indicator <- which(components == "alternative")
+
+            # compute the number of conditional samples
+            n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% this_component_indicator)
+
+            # replace null samples with NAs (important for later transformations)
+            model_samples[!model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% this_component_indicator, par_names] <- NA
+
+            # add warnings about conditional summary
+            warnings <- c(warnings, .runjags_conditional_warning(par_names, n_conditional_samples))
+
+          }else{
+
+            # remove the join samples and replace with individual conditional samples
+            temp_position    <- min(which(colnames(model_samples) %in% par))
+            temp_all_samples <- model_samples[, colnames(model_samples) %in% par,drop=FALSE]
+            temp_new_samples <- list()
+            model_samples    <- model_samples[,!colnames(model_samples) %in% par,drop=FALSE]
+
+             # component-by-component replacement
+            for(component in unique(components[components != "null"])){
+
+              # create component specific samples
+              temp_par_names <- paste0(par_names, "[", component, "]")
+              temp_new_samples[[component]]           <- temp_all_samples
+              colnames(temp_new_samples[[component]]) <- temp_par_names
+
+              # select the corresponding indicators
+              this_component_indicator <- which(components == component)
+
+              # compute the number of conditional samples
+              n_conditional_samples <- sum(model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% this_component_indicator)
+
+              # replace null samples with NAs (important for later transformations)
+              temp_new_samples[[component]][!model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% this_component_indicator,] <- NA
+
+              # add warnings about conditional summary
+              warnings <- c(warnings, .runjags_conditional_warning(temp_par_names, n_conditional_samples))
+
+              # forward transformations to the conditional estimates
+              if(!is.null(transformations[[par]])){
+                transformations[[temp_par_names]] <- transformations[[par]]
+                prior_list[[temp_par_names]]      <- prior_list[[par]][which(components == component)][1]
+              }
+            }
+
+            # place the transformed samples back
+            model_samples <- cbind(
+              if(temp_position > 1) model_samples[,1:(temp_position-1),drop=FALSE],
+              do.call(cbind, temp_new_samples),
+              if(temp_position <= ncol(model_samples)) model_samples[,temp_position:ncol(model_samples),drop=FALSE]
+            )
+
+            # remove the original parameter transformations
+            if(!is.null(transformations[[par]])){
+              transformations[[par]] <- NULL
+              prior_list[[par]]      <- NULL
+            }
+          }
+        }
+      }
+
+      # remove/rename the inclusions probabilities
+      if(remove_inclusion){
+        model_samples   <- model_samples[,colnames(model_samples) != paste0(par, "_indicator"),drop=FALSE]
+      }else{
+        if(all(components %in% c("null", "alternative"))){
+          # replace and rename in the samples
+          model_samples[,colnames(model_samples) == paste0(par, "_indicator")] <- ifelse(
+            model_samples[,colnames(model_samples) == paste0(par, "_indicator")] %in% which(components == "alternative"), 1, 0)
+          colnames(model_samples)[colnames(model_samples) == paste0(par, "_indicator")] <- paste0(par, " (inclusion)")
+        }else{
+          # extract
+          temp_position <- min(which(colnames(model_samples) %in% paste0(par, "_indicator")))
+          temp_samples  <- model_samples[,colnames(model_samples) == paste0(par, "_indicator")]
+          model_samples <- model_samples[,colnames(model_samples) != paste0(par, "_indicator"),drop=FALSE]
+
+          # compute component specific indicators
+          temp_new_samples <- lapply(unique(components), function(component) ifelse(temp_samples %in% which(components == component), 1, 0))
+          temp_new_samples <- do.call(cbind, temp_new_samples)
+          colnames(temp_new_samples) <- paste0(par, " (inclusion: ", unique(components),")")
+
+          # place the transformed samples back
+          model_samples <- cbind(
+            if(temp_position > 1) model_samples[,1:(temp_position-1),drop=FALSE],
+            temp_new_samples,
+            if(temp_position <= ncol(model_samples)) model_samples[,temp_position:ncol(model_samples),drop=FALSE]
+          )
+        }
+      }
     }
   }
 
+  # remove transformations for removed variables
+  if(!is.null(transformations)){
+    transformations <-  transformations[names(transformations) %in% names(prior_list)]
+  }
+
   # apply transformations (not orthornormal if they are to be returned transformed to diffs)
   if(!is.null(transformations)){
     for(par in names(transformations)){
@@ -855,13 +1029,6 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
 
         # non-factor priors
         model_samples[,par] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[,par]), transformations[[par]][["arg"]]))
-        runjags_summary[par, "Mean"]    <- mean(model_samples[,par], na.rm = TRUE)
-        runjags_summary[par, "SD"]      <- sd(model_samples[,par], na.rm = TRUE)
-        runjags_summary[par, "Lower95"] <- stats::quantile(model_samples[,par], .025, na.rm = TRUE)
-        runjags_summary[par, "Upper95"] <- stats::quantile(model_samples[,par], .975, na.rm = TRUE)
-        runjags_summary[par, "Median"]  <- do.call(transformations[[par]][["fun"]], c(list(runjags_summary[par, "Median"]), transformations[[par]][["arg"]]))
-        runjags_summary[par, "MCerr"]   <- do.call(transformations[[par]][["fun"]], c(list(runjags_summary[par, "MCerr"]), transformations[[par]][["arg"]]))
-        runjags_summary[par, "MC.ofSD"] <- 100 * runjags_summary[par, "MCerr"] / runjags_summary[par, "SD"]
 
       }else if((!transform_factors && (is.prior.orthonormal(prior_list[[par]]) |  is.prior.meandif(prior_list[[par]]))) || is.prior.treatment(prior_list[[par]])){
 
@@ -870,17 +1037,8 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
 
         for(i in seq_along(par_names)){
           model_samples[,par_names[i]] <- do.call(transformations[[par]][["fun"]], c(list(model_samples[,par_names[i]]), transformations[[par]][["arg"]]))
-          runjags_summary[par_names[i], "Mean"]    <- mean(model_samples[,par_names[i]], na.rm = TRUE)
-          runjags_summary[par_names[i], "SD"]      <- sd(model_samples[,par_names[i]], na.rm = TRUE)
-          runjags_summary[par_names[i], "Lower95"] <- stats::quantile(model_samples[,par_names[i]], .025, na.rm = TRUE)
-          runjags_summary[par_names[i], "Upper95"] <- stats::quantile(model_samples[,par_names[i]], .975, na.rm = TRUE)
-          runjags_summary[par_names[i], "Median"]  <- do.call(transformations[[par]][["fun"]], c(list(runjags_summary[par_names[i], "Median"]), transformations[[par]][["arg"]]))
-          runjags_summary[par_names[i], "MCerr"]   <- do.call(transformations[[par]][["fun"]], c(list(runjags_summary[par_names[i], "MCerr"]), transformations[[par]][["arg"]]))
-          runjags_summary[par_names[i], "MC.ofSD"] <- 100 * runjags_summary[par_names[i], "MCerr"] / runjags_summary[par_names[i], "SD"]
         }
-
       }
-
     }
   }
 
@@ -891,12 +1049,14 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
 
       par_names <- .JAGS_prior_factor_names(par, prior_list[[par]])
 
-      original_samples <- model_samples[,par_names,drop = FALSE]
+      temp_position <- min(which(colnames(model_samples) %in% par_names))
+      temp_samples  <- model_samples[, colnames(model_samples) %in% par_names,drop=FALSE]
+      model_samples <- model_samples[,!colnames(model_samples) %in% par_names,drop=FALSE]
 
       if(is.prior.orthonormal(prior_list[[par]])){
-        transformed_samples <- original_samples %*% t(contr.orthonormal(1:(.get_prior_factor_levels(prior_list[[par]])+1)))
+        transformed_samples <- temp_samples %*% t(contr.orthonormal(1:(.get_prior_factor_levels(prior_list[[par]])+1)))
       }else if(is.prior.meandif(prior_list[[par]])){
-        transformed_samples <- original_samples %*% t(contr.meandif(1:(.get_prior_factor_levels(prior_list[[par]])+1)))
+        transformed_samples <- temp_samples %*% t(contr.meandif(1:(.get_prior_factor_levels(prior_list[[par]])+1)))
       }
 
       # apply transformation if specified
@@ -918,74 +1078,29 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
       }
       colnames(transformed_samples) <- transformed_names
 
-      # update samples
-      model_samples <- model_samples[,!colnames(model_samples) %in% par_names,drop=FALSE]
-      model_samples <- cbind(model_samples, transformed_samples)
-
-      # update summary
-      if(anyNA(transformed_samples)){
-        # remove NA's introduced by conditional models and spike & slab priors -- also removes the
-        transformed_chains  <- lapply(split(data.frame(transformed_samples), sort(rep(1:length(fit[["mcmc"]]), fit[["sample"]]))), function(x) coda::mcmc(stats::na.omit(x)))
-        transformed_summary <- summary(runjags::combine.mcmc(transformed_chains, collapse.chains = FALSE))
-        transformed_summary <- cbind(
-          Lower95 = transformed_summary$quantiles[,"2.5%"],
-          Median  = transformed_summary$quantiles[,"50%"],
-          Upper95 = transformed_summary$quantiles[,"97.5%"],
-          Mean    = transformed_summary$statistics[,"Mean"],
-          SD      = transformed_summary$statistics[,"SD"],
-          Mode    = NA,
-          MCerr   = NA,
-          MC.ofSD = NA,
-          SSeff   = NA,
-          AC.10   = NA,
-          psrf    = NA
-        )
-      }else{
-        transformed_chains  <- lapply(split(data.frame(transformed_samples), sort(rep(1:length(fit[["mcmc"]]), fit[["sample"]]))), coda::mcmc)
-        transformed_summary <- summary(runjags::combine.mcmc(transformed_chains, collapse.chains = FALSE))
-        transformed_summary <- cbind(
-          Lower95 = transformed_summary$quantiles[,"2.5%"],
-          Median  = transformed_summary$quantiles[,"50%"],
-          Upper95 = transformed_summary$quantiles[,"97.5%"],
-          Mean    = transformed_summary$statistics[,"Mean"],
-          SD      = transformed_summary$statistics[,"SD"],
-          Mode    = NA,
-          MCerr   = if(is.prior.point(prior_list[[par]])) NA else transformed_summary$statistics[,"Naive SE"],
-          MC.ofSD = if(is.prior.point(prior_list[[par]])) NA else 100 * transformed_summary$statistics[,"Naive SE"] / transformed_summary$statistics[,"SD"],
-          SSeff   = if(is.prior.point(prior_list[[par]])) NA else unname(coda::effectiveSize(coda::as.mcmc(transformed_samples))),
-          AC.10   = if(is.prior.point(prior_list[[par]])) NA else coda::autocorr.diag(coda::as.mcmc(transformed_samples), lags = 10)[1,],
-          psrf    = if(is.prior.point(prior_list[[par]])) NA else if(length(fit$mcmc) > 1) unname(coda::gelman.diag(transformed_chains, multivariate = FALSE)$psrf[,"Point est."]) else NA
-        )
-      }
-
-      rownames(transformed_summary) <- transformed_names
-
-      par_index       <- which.max(rownames(runjags_summary) %in% par_names)
-      runjags_summary <- runjags_summary[!rownames(runjags_summary) %in% par_names,]
-      runjags_summary <- rbind(
-        if(par_index > 1) runjags_summary[1:(par_index-1),],
-        transformed_summary,
-        if(par_index <= nrow(runjags_summary)) runjags_summary[par_index:nrow(runjags_summary),]
+      # place the transformed samples back
+      model_samples <- cbind(
+        if(temp_position > 1) model_samples[,1:(temp_position-1),drop=FALSE],
+        transformed_samples,
+        if(temp_position <= ncol(model_samples)) model_samples[,temp_position:ncol(model_samples),drop=FALSE]
       )
+
     }
   }
 
-  # remove un-wanted columns
-  runjags_summary <- runjags_summary[,!colnames(runjags_summary) %in% c("Mode", "AC.10"),drop = FALSE]
-
   # rename treatment factor levels
   if(any(sapply(prior_list, is.prior.treatment))){
     for(par in names(prior_list)[sapply(prior_list, is.prior.treatment)]){
       if(!.is_prior_interaction(prior_list[[par]])){
         if(.get_prior_factor_levels(prior_list[[par]]) == 1){
-          rownames(runjags_summary)[rownames(runjags_summary) == par] <-
+          colnames(model_samples)[colnames(model_samples) == par] <-
             paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[-1], "]")
         }else{
-          rownames(runjags_summary)[rownames(runjags_summary) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
+          colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
             paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[-1], "]")
         }
       }else if(length(attr(prior_list[[par]], "levels")) == 1){
-        rownames(runjags_summary)[rownames(runjags_summary) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
+        colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
           paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[[1]][-1], "]")
       }
     }
@@ -996,49 +1111,46 @@ runjags_estimates_table  <- function(fit, transformations = NULL, title = NULL,
     for(par in names(prior_list)[sapply(prior_list, is.prior.independent)]){
       if(!.is_prior_interaction(prior_list[[par]])){
         if(.get_prior_factor_levels(prior_list[[par]]) == 1){
-          rownames(runjags_summary)[rownames(runjags_summary) == par] <-
+          colnames(model_samples)[colnames(model_samples) == par] <-
             paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]]), "]")
         }else{
-          rownames(runjags_summary)[rownames(runjags_summary) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
+          colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
             paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]]), "]")
         }
       }else if(length(attr(prior_list[[par]], "levels")) == 1){
-        rownames(runjags_summary)[rownames(runjags_summary) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
+        colnames(model_samples)[colnames(model_samples) %in% paste0(par,"[",1:.get_prior_factor_levels(prior_list[[par]]),"]")] <-
           paste0(par,"[",.get_prior_factor_level_names(prior_list[[par]])[[1]], "]")
       }
     }
   }
 
   # store parameter names before removing formula attachments
-  parameter_names <- rownames(runjags_summary)
+  parameter_names <- colnames(model_samples)
 
   # rename formula parameters
   if(any(!sapply(lapply(prior_list, attr, which = "parameter"), is.null))){
-    rownames(runjags_summary) <- format_parameter_names(
-      parameters         = rownames(runjags_summary),
+    colnames(model_samples) <- format_parameter_names(
+      parameters         = colnames(model_samples),
       formula_parameters = unique(unlist(lapply(prior_list, attr, which = "parameter"))),
       formula_prefix     = formula_prefix)
   }
 
+  # return samples if requested
+  if(return_samples){
+    attr(model_samples, "prior_list") <- prior_list
+    return(model_samples)
+  }
 
-  # rename the rest
-  colnames(runjags_summary)[colnames(runjags_summary) == "Lower95"] <- "lCI"
-  colnames(runjags_summary)[colnames(runjags_summary) == "Upper95"] <- "uCI"
-  colnames(runjags_summary)[colnames(runjags_summary) == "MCerr"]   <- "MCMC_error"
-  colnames(runjags_summary)[colnames(runjags_summary) == "MC.ofSD"] <- "MCMC_SD_error"
-  colnames(runjags_summary)[colnames(runjags_summary) == "SSeff"]   <- "ESS"
-  colnames(runjags_summary)[colnames(runjags_summary) == "psrf"]    <- "R_hat"
-
-  # change the SD error to a fraction
-  runjags_summary[, "MCMC_SD_error"] <- runjags_summary[, "MCMC_SD_error"] / 100
-
-  # reorder the columns
-  runjags_summary <- runjags_summary[,c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"), drop = FALSE]
-  runjags_summary <- data.frame(runjags_summary)
+  # compute the summary
+  if(ncol(model_samples) == 0){
+    return(runjags_estimates_empty_table(title = title, footnotes = footnotes, warnings = warnings))
+  }else{
+    runjags_summary <- .runjags_summary_fast(model_samples, n_samples = fit$sample, n_chains = length(fit$mcmc), conditional = conditional)
+  }
 
   # prepare output
   class(runjags_summary)              <- c("BayesTools_table", "BayesTools_runjags_summary", class(runjags_summary))
-  attr(runjags_summary, "type")       <- c(rep("estimate", 5), "MCMC_error", "MCMC_SD_error", "ESS", "R_hat")
+  attr(runjags_summary, "type")       <- c(rep("estimate", 5), if(!conditional) c("MCMC_error", "MCMC_SD_error", "ESS", "R_hat"))
   attr(runjags_summary, "parameters") <- parameter_names
   attr(runjags_summary, "rownames")   <- TRUE
   attr(runjags_summary, "title")      <- title
@@ -1066,7 +1178,7 @@ runjags_inference_table  <- function(fit, title = NULL, footnotes = NULL, warnin
   check_bool(formula_prefix, "formula_prefix")
 
   # return empty table if none of the priors is spike and slab
-  if(!any(sapply(prior_list, is.prior.spike_and_slab))){
+  if(!any(sapply(prior_list, function(p) is.prior.spike_and_slab(p) | is.prior.mixture(p)))){
     runjags_summary <- runjags_inference_empty_table(title = title, footnotes = footnotes, warnings = warnings)
     return(runjags_summary)
   }
@@ -1087,20 +1199,69 @@ runjags_inference_table  <- function(fit, title = NULL, footnotes = NULL, warnin
         post_prob    = temp_post_prob,
         inclusion_BF = (temp_post_prob / (1-temp_post_prob))  /  (temp_prior_prob / (1-temp_prior_prob))
       ))
+    }else if(is.prior.mixture(prior_list[[par]])){
+
+      # extract the components and prior probabilities
+      components      <- attr(prior_list[[par]], "components")
+      temp_prior_prob <- attr(prior_list[[par]], "prior_weights")
+      temp_prior_prob <- sapply(unique(components), function(component) sum(temp_prior_prob[which(components == component)])) / sum(temp_prior_prob)
+      temp_post_prob  <- sapply(unique(components), function(component) mean(model_samples[,paste0(par, "_indicator")] %in% which(components == component)))
+
+      # if only null and alternative are specified, removed the null component
+      if(all(components %in% c("null", "alternative"))){
+
+        if(all(components == "null")){
+          temp_prior_prob <- c(temp_prior_prob, "alternative" = 0)
+          temp_post_prob  <- c(temp_post_prob,  "alternative" = 0)
+        }
+        if(all(components == "alternative")){
+          temp_prior_prob <- c("null" = 0, temp_prior_prob)
+          temp_post_prob  <- c("null" = 0,  temp_post_prob)
+        }
+
+        runjags_summary <- rbind(runjags_summary, data.frame(
+          Parameter    = par,
+          prior_prob   = temp_prior_prob[["alternative"]],
+          post_prob    = temp_post_prob[["alternative"]],
+          inclusion_BF = inclusion_BF(prior_probs = temp_prior_prob, post_probs = temp_post_prob, is_null = names(temp_post_prob) != "alternative")
+        ))
+
+      }else{
+
+        # compute summary for each component
+        for(component in unique(components)){
+          runjags_summary <- rbind(runjags_summary, data.frame(
+            Parameter    = paste0(par, " [", component, "]"),
+            prior_prob   = temp_prior_prob[[component]],
+            post_prob    = temp_post_prob[[component]],
+            inclusion_BF = inclusion_BF(prior_probs = temp_prior_prob, post_probs = temp_post_prob, is_null = names(temp_post_prob) != component)
+          ))
+        }
+
+      }
     }
   }
 
-  runjags_summary$Parameter <- format_parameter_names(
-    parameters         = runjags_summary$Parameter,
-    formula_parameters = unique(unlist(lapply(prior_list, attr, which = "parameter"))),
-    formula_prefix     = formula_prefix)
+  # store parameter names before removing formula attachments
+  parameter_names           <- runjags_summary$Parameter
+  rownames(runjags_summary) <- parameter_names
+  runjags_summary           <- runjags_summary[,-1]
 
-  class(runjags_summary)             <- c("BayesTools_table", "BayesTools_runjags_summary", class(runjags_summary))
-  attr(runjags_summary, "type")      <- c("string", "prior_prob", "post_prob", "inclusion_BF")
-  attr(runjags_summary, "rownames")  <- FALSE
-  attr(runjags_summary, "title")     <- title
-  attr(runjags_summary, "footnotes") <- footnotes
-  attr(runjags_summary, "warnings")  <- warnings
+  # rename formula parameters
+  if(any(!sapply(lapply(prior_list, attr, which = "parameter"), is.null))){
+    rownames(runjags_summary) <- format_parameter_names(
+      parameters         = rownames(runjags_summary),
+      formula_parameters = unique(unlist(lapply(prior_list, attr, which = "parameter"))),
+      formula_prefix     = formula_prefix)
+  }
+
+  class(runjags_summary)               <- c("BayesTools_table", "BayesTools_runjags_inference", class(runjags_summary))
+  attr(runjags_summary, "type")        <- c("prior_prob", "post_prob", "inclusion_BF")
+  attr(runjags_summary, "parameters")  <- parameter_names
+  attr(runjags_summary, "rownames")    <- TRUE
+  attr(runjags_summary, "title")       <- title
+  attr(runjags_summary, "footnotes")   <- footnotes
+  attr(runjags_summary, "warnings")    <- warnings
 
   return(runjags_summary)
 }
@@ -1169,11 +1330,11 @@ runjags_estimates_empty_table <- function(title = NULL, footnotes = NULL, warnin
 #' @rdname BayesTools_model_tables
 runjags_inference_empty_table <- function(title = NULL, footnotes = NULL, warnings = NULL){
 
-  empty_table <- data.frame(matrix(nrow = 0, ncol = 4))
-  colnames(empty_table) <- c("Parameter", "prior_prob", "post_prob", "inclusion_BF")
+  empty_table <- data.frame(matrix(nrow = 0, ncol = 3))
+  colnames(empty_table) <- c("prior_prob", "post_prob", "inclusion_BF")
 
-  class(empty_table)             <- c("BayesTools_table", "BayesTools_runjags_summary", class(empty_table))
-  attr(empty_table, "type")      <- c("string", "prior_prob", "post_prob", "inclusion_BF")
+  class(empty_table)             <- c("BayesTools_table", "BayesTools_runjags_inference", class(empty_table))
+  attr(empty_table, "type")      <- c("prior_prob", "post_prob", "inclusion_BF")
   attr(empty_table, "rownames")  <- FALSE
   attr(empty_table, "title")     <- title
   attr(empty_table, "footnotes") <- footnotes
@@ -1264,7 +1425,6 @@ stan_estimates_table  <- function(fit, transformations = NULL, title = NULL, foo
   return(stan_summary)
 }
 
-
 #' @title Print a BayesTools table
 #'
 #' @param x a BayesTools_values_tables
@@ -1451,6 +1611,53 @@ remove_column <- function(table, column_position = NULL){
   return(new_table)
 }
 
+#' @title Updates BayesTools table
+#'
+#' @description Updates BayesTools table while not breaking formatting, attributes, etc...
+#'
+#' @param object a BayesTools table
+#' @param title title of the table
+#' @param footnotes add footnotes to the table
+#' @param warnings add warnings of the table
+#' @param remove_parameters remove parameters from the table
+#' @param logBF whether to format Bayes factors as log(BF)
+#' @param BF01 whether to format Bayes factors as 1/BF
+#' @param ... additional arguments.
+#'
+#' @return returns an object of 'BayesTools_table' class.
+#' @export
+update.BayesTools_table <- function(object, title = NULL, footnotes = NULL, warnings = NULL, remove_parameters = NULL, logBF = FALSE, BF01 = FALSE, ...){
+
+  check_char(title, "title", allow_NULL = TRUE)
+  check_char(footnotes, "footnotes", check_length = 0, allow_NULL = TRUE)
+  check_char(warnings, "warnings", check_length = 0, allow_NULL = TRUE)
+  check_list(remove_parameters, "remove_parameters", allow_NULL = TRUE)
+  check_bool(logBF, "logBF")
+  check_bool(BF01,  "BF01")
+
+  if(!is.null(footnotes)){
+    attr(object, "footnotes") <- c(attr(object, "footnotes"), footnotes)
+  }
+
+  if(!is.null(warnings)){
+    attr(object, "warnings")  <- c(attr(object, "warnings"), warnings)
+  }
+
+  if(!is.null(remove_parameters)){
+    object <- object[rownames(object) %in% remove_parameters,,drop=FALSE]
+  }
+
+  if(!is.null(title)){
+    attr(object, "title") <- title
+  }
+
+  if(any(attr(object, "type") == "inclusion_BF")){
+    object[,which(attr(object, "type") == "inclusion_BF")] <- format_BF(as.numeric(object[,which(attr(object, "type") == "inclusion_BF")]), logBF = logBF, BF01 = BF01, inclusion = TRUE)
+  }
+
+  return(object)
+}
+
 .format_column       <- function(x, type, n_models){
   if(is.null(x) || length(x) == 0){
     return(x)
@@ -1555,7 +1762,7 @@ remove_column <- function(table, column_position = NULL){
   return(x)
 }
 
-.check_table_types <- function(x, name, allow_NULL = FALSE){
+.check_table_types      <- function(x, name, allow_NULL = FALSE){
   check_char(x, name, allow_values = c(
     "integer", "prior", "string_left", "string", "estimate",
     "probability", "prior_prob", "post_prob",
@@ -1563,3 +1770,63 @@ remove_column <- function(table, column_position = NULL){
     "ESS", "R_hat", "MCMC_error", "MCMC_SD_error", "min_ESS", "max_R_hat", "max_MCMC_error", "max_MCMC_SD_error"),
     allow_NULL = allow_NULL)
 }
+.runjags_summary_fast   <- function(model_samples, n_samples, n_chains, conditional){
+
+  # the chains needs to be kept merged for conditional summary (due to NAs in the chains)
+  runjags_summary <- cbind.data.frame(
+    "Mean"    = apply(model_samples, 2, mean,                           na.rm = TRUE),
+    "SD"      = apply(model_samples, 2, stats::sd,                      na.rm = TRUE),
+    "lCI"     = apply(model_samples, 2, stats::quantile, probs = 0.025, na.rm = TRUE),
+    "Median"  = apply(model_samples, 2, stats::median,                  na.rm = TRUE),
+    "uCI"     = apply(model_samples, 2, stats::quantile, probs = 0.975, na.rm = TRUE)
+  )
+
+  # remove all but Mean for inclusions
+  runjags_summary[grepl("(inclusion)", rownames(runjags_summary)), c("SD", "lCI", "Median", "uCI")] <- NA
+
+  # don't produce fit diagnostics for conditional samples (different chain lengths etc...)
+  if(conditional){
+    return(runjags_summary)
+  }
+
+  # split back the chains (allows for diagnostics calculation)
+  model_samples_list <- split(as.data.frame(model_samples), rep(1:n_chains, each = n_samples), drop = FALSE)
+  model_samples_list <- coda::as.mcmc.list(lapply(model_samples_list, coda::as.mcmc))
+  mcmc_summary       <- summary(model_samples_list, quantiles = NULL)$statistics
+
+  # fix single parameter summaries
+  if(is.null(dim(mcmc_summary))){
+    mcmc_summary <- t(mcmc_summary)
+  }
+
+  # add diagnostics to non-conditional samples
+  runjags_summary <- cbind.data.frame(
+    runjags_summary,
+    "MCMC_error"    = mcmc_summary[,"Time-series SE"],
+    "MCMC_SD_error" = mcmc_summary[,"Time-series SE"] / mcmc_summary[,"SD"],
+    "ESS"           = coda::effectiveSize(model_samples_list),
+    "R_hat"         = if(n_chains > 1) coda::gelman.diag(model_samples_list, multivariate = FALSE, autoburnin = FALSE)$psrf[,1] else NA
+  )
+
+  # remove incorrect NANs and NAs from the diagnostics
+  runjags_summary[is.nan(runjags_summary[,"MCMC_SD_error"]),"MCMC_SD_error"] <- NA
+  runjags_summary[runjags_summary[,"ESS"] == 0, "ESS"]                       <- 0
+  runjags_summary[is.nan(runjags_summary[,"R_hat"]),"R_hat"]                 <- NA
+
+  # first omega parameter is always constant
+  runjags_summary[grepl("omega[0,", rownames(runjags_summary), fixed = TRUE), c("MCMC_error", "MCMC_SD_error", "ESS", "R_hat")] <- NA
+
+  # remove diagnostics for inclusions
+  runjags_summary[grepl("(inclusion)", rownames(runjags_summary)), c("MCMC_error", "MCMC_SD_error", "ESS", "R_hat")] <- NA
+
+  return(runjags_summary)
+}
+.runjags_conditional_warning <- function(parameters, n_samples, warning_limit = 500){
+  if(n_samples == 0){
+    return(sprintf("Conditional summary for %1$s parameter could not be computed due to no posterior samples.", paste0(parameters, collapse = ", ")))
+  }else if(n_samples <= warning_limit){
+    return(sprintf("Conditional summary for %1$s is based on %2$i samples.", paste0(parameters, collapse = ", "), n_samples))
+  }else{
+    return()
+  }
+}
diff --git a/man/BayesTools.Rd b/man/BayesTools.Rd
index 62f9dd35..ce670a2a 100644
--- a/man/BayesTools.Rd
+++ b/man/BayesTools.Rd
@@ -3,7 +3,6 @@
 \docType{package}
 \name{BayesTools}
 \alias{BayesTools}
-\alias{_PACKAGE}
 \alias{BayesTools-package}
 \title{BayesTools}
 \description{
diff --git a/man/BayesTools_model_tables.Rd b/man/BayesTools_model_tables.Rd
index 525d7eda..4836c9d6 100644
--- a/man/BayesTools_model_tables.Rd
+++ b/man/BayesTools_model_tables.Rd
@@ -40,7 +40,8 @@ runjags_estimates_table(
   transform_orthonormal = FALSE,
   formula_prefix = TRUE,
   remove_inclusion = FALSE,
-  remove_parameters = NULL
+  remove_parameters = NULL,
+  return_samples = FALSE
 )
 
 runjags_inference_table(
@@ -63,7 +64,8 @@ JAGS_estimates_table(
   transform_orthonormal = FALSE,
   formula_prefix = TRUE,
   remove_inclusion = FALSE,
-  remove_parameters = NULL
+  remove_parameters = NULL,
+  return_samples = FALSE
 )
 
 JAGS_inference_table(
@@ -156,6 +158,9 @@ differences from the grand mean}
 
 \item{remove_inclusion}{whether estimates of the inclusion probabilities
 should be excluded from the summary table. Defaults to \code{FALSE}.}
+
+\item{return_samples}{whether to return the transoformed and formated samples
+instead of the table. Defaults to \code{FALSE}.}
 }
 \value{
 \code{model_summary_table} returns a table with
diff --git a/man/JAGS_check_convergence.Rd b/man/JAGS_check_convergence.Rd
index 647035de..1f92a126 100644
--- a/man/JAGS_check_convergence.Rd
+++ b/man/JAGS_check_convergence.Rd
@@ -10,7 +10,9 @@ JAGS_check_convergence(
   max_Rhat = 1.05,
   min_ESS = 500,
   max_error = 0.01,
-  max_SD_error = 0.05
+  max_SD_error = 0.05,
+  add_parameters = NULL,
+  fail_fast = FALSE
 )
 }
 \arguments{
@@ -28,6 +30,11 @@ Defaults to \code{1.05}.}
 
 \item{max_SD_error}{maximum MCMC error as the proportion of standard
 deviation of the parameters. Defaults to \code{0.05}.}
+
+\item{add_parameters}{vector of additional parameter names that should be used
+(only allows removing last, fixed, omega element if omega is tracked manually).}
+
+\item{fail_fast}{whether the function should stop after the first failed convergence check.}
 }
 \value{
 \code{JAGS_check_convergence} returns a boolean
diff --git a/man/as_marginal_inference.Rd b/man/as_marginal_inference.Rd
new file mode 100644
index 00000000..1bc74da8
--- /dev/null
+++ b/man/as_marginal_inference.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/marginal-distributions.R
+\name{as_marginal_inference}
+\alias{as_marginal_inference}
+\title{Model-average marginal posterior distributions and
+marginal Bayes factors based on BayesTools JAGS model via \code{marginal_inference}}
+\usage{
+as_marginal_inference(
+  model,
+  marginal_parameters,
+  parameters,
+  conditional_list,
+  conditional_rule,
+  formula,
+  null_hypothesis = 0,
+  normal_approximation = FALSE,
+  n_samples = 10000,
+  silent = FALSE,
+  force_plots = FALSE
+)
+}
+\arguments{
+\item{model}{model fit via the \link{JAGS_fit} function}
+
+\item{marginal_parameters}{parameters for which the the marginal summary
+should be created}
+
+\item{parameters}{all parameters included in the model_list that are
+relevant for the formula (all of which need to have specification of
+\code{is_null_list})}
+
+\item{conditional_list}{list of conditional parameters for each marginal parameter}
+
+\item{conditional_rule}{a character string specifying the rule for conditioning.
+Either "AND" or "OR". Defaults to "AND".}
+
+\item{formula}{model formula (needs to be specified if \code{parameter} was part of a formula)}
+
+\item{null_hypothesis}{point null hypothesis to test. Defaults to \code{0}}
+
+\item{normal_approximation}{whether the height of prior and posterior density should be
+approximated via a normal distribution (rather than kernel density). Defaults to \code{FALSE}.}
+
+\item{n_samples}{number of samples to be drawn for the model-averaged
+prior distribution}
+
+\item{silent}{whether warnings should be returned silently. Defaults to \code{FALSE}}
+
+\item{force_plots}{temporal argument allowing to generate conditional posterior samples
+suitable for prior and posterior plots. Only available when conditioning on a
+single parameter.}
+}
+\value{
+\code{as_marginal_inference} returns an object of class 'marginal_inference'.
+}
+\description{
+Creates marginal model-averaged and conditional
+posterior distributions based on a BayesTools JAGS model, vector of parameters,
+formula, and a list of conditional specifications for each parameter.
+Computes inclusion Bayes factors for each marginal estimate via a Savage-Dickey
+density approximation.
+}
+\seealso{
+\link{marginal_inference} \link{as_mixed_posteriors}
+}
diff --git a/man/as_mixed_posteriors.Rd b/man/as_mixed_posteriors.Rd
new file mode 100644
index 00000000..77bb85c3
--- /dev/null
+++ b/man/as_mixed_posteriors.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/model-averaging.R
+\name{as_mixed_posteriors}
+\alias{as_mixed_posteriors}
+\title{Export BayesTools JAGS model posterior distribution as model-average posterior distributions via \code{mix_posteriors}}
+\usage{
+as_mixed_posteriors(
+  model,
+  parameters,
+  conditional = NULL,
+  conditional_rule = "AND",
+  force_plots = FALSE
+)
+}
+\arguments{
+\item{model}{model fit via the \link{JAGS_fit} function}
+
+\item{parameters}{vector of parameters names for which inference should
+be drawn}
+
+\item{conditional}{a character vector of parameters to be conditioned on}
+
+\item{conditional_rule}{a character string specifying the rule for conditioning.
+Either "AND" or "OR". Defaults to "AND".}
+
+\item{force_plots}{temporal argument allowing to generate conditional posterior samples
+suitable for prior and posterior plots. Only available when conditioning on a
+single parameter.}
+}
+\value{
+\code{as_mix_posteriors} returns a named list of mixed posterior
+distributions (either a vector of matrix).
+}
+\description{
+Creates a model-averages posterior distributions on a single
+model that allows mimicking the \link{mix_posteriors} functionality. This function
+is useful when the model-averaged ensemble is based on \link{prior_spike_and_slab}
+or \link{prior_mixture} priors - the model-averaging is done within the model.
+}
+\seealso{
+\link{mix_posteriors}
+}
diff --git a/man/interpret.Rd b/man/interpret.Rd
index a170d376..0891cff7 100644
--- a/man/interpret.Rd
+++ b/man/interpret.Rd
@@ -2,9 +2,12 @@
 % Please edit documentation in R/interpret.R
 \name{interpret}
 \alias{interpret}
+\alias{interpret2}
 \title{Interpret ensemble inference and estimates}
 \usage{
 interpret(inference, samples, specification, method)
+
+interpret2(specification, method = NULL)
 }
 \arguments{
 \item{inference}{model inference created by \link{ensemble_inference}}
diff --git a/man/is.prior.Rd b/man/is.prior.Rd
index 004a0a5c..f23a4da3 100644
--- a/man/is.prior.Rd
+++ b/man/is.prior.Rd
@@ -16,6 +16,7 @@
 \alias{is.prior.independent}
 \alias{is.prior.spike_and_slab}
 \alias{is.prior.meandif}
+\alias{is.prior.mixture}
 \title{Reports whether x is a a prior object}
 \usage{
 is.prior(x)
@@ -47,6 +48,8 @@ is.prior.independent(x)
 is.prior.spike_and_slab(x)
 
 is.prior.meandif(x)
+
+is.prior.mixture(x)
 }
 \arguments{
 \item{x}{an object of test}
diff --git a/man/marginal_inference.Rd b/man/marginal_inference.Rd
index dfe92a52..e03cd552 100644
--- a/man/marginal_inference.Rd
+++ b/man/marginal_inference.Rd
@@ -43,15 +43,14 @@ corresponding to the null hypothesis)}
 approximated via a normal distribution (rather than kernel density). Defaults to \code{FALSE}.}
 
 \item{n_samples}{number of samples to be drawn for the model-averaged
-posterior distribution}
+prior distribution}
 
 \item{seed}{seed for random number generation}
 
 \item{silent}{whether warnings should be returned silently. Defaults to \code{FALSE}}
 }
 \value{
-\code{mix_posteriors} returns a named list of mixed posterior
-distributions (either a vector of matrix).
+\code{marginal_inference} returns an object of class 'marginal_inference'.
 }
 \description{
 Creates marginal model-averaged and conditional
diff --git a/man/marginal_posterior.Rd b/man/marginal_posterior.Rd
index 8bfab341..3b6a1aea 100644
--- a/man/marginal_posterior.Rd
+++ b/man/marginal_posterior.Rd
@@ -54,7 +54,7 @@ settings the \code{x_seq} or \code{x_range} was specified on
 the transformed support}
 
 \item{n_samples}{number of samples to be drawn for the model-averaged
-posterior distribution}
+prior distribution}
 
 \item{...}{additional arguments}
 }
diff --git a/man/mix_posteriors.Rd b/man/mix_posteriors.Rd
index 3f365fe4..998e9f38 100644
--- a/man/mix_posteriors.Rd
+++ b/man/mix_posteriors.Rd
@@ -46,5 +46,5 @@ indicators of the null or alternative hypothesis models
 for each parameter.
 }
 \seealso{
-\link{ensemble_inference} \link{BayesTools_ensemble_tables}
+\link{ensemble_inference} \link{BayesTools_ensemble_tables} \link{as_mixed_posteriors}
 }
diff --git a/man/prior_mixture.Rd b/man/prior_mixture.Rd
new file mode 100644
index 00000000..224c830c
--- /dev/null
+++ b/man/prior_mixture.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/priors.R
+\name{prior_mixture}
+\alias{prior_mixture}
+\title{Creates a mixture of prior distributions}
+\usage{
+prior_mixture(
+  prior_list,
+  is_null = rep(FALSE, length(prior_list)),
+  components = NULL
+)
+}
+\arguments{
+\item{prior_list}{a list of prior distributions to be mixed.}
+
+\item{is_null}{a logical vector indicating which of the prior distributions
+should be considered as a null distribution. Defaults to \code{rep(FALSE, length(prior_list))}.}
+
+\item{components}{a character vector indicating which of the prior distributions
+belong to the same mixture component (this is an alternative specification to the \code{is_null} argument).
+Defaults to \code{NULL} (i.e., \code{is_null} is used.}
+}
+\description{
+\code{prior_mixture} creates a mixture of prior distributions.
+This is a more generic version of the \code{prior_spike_and_slab} function.
+}
+\seealso{
+\code{\link[=prior]{prior()}}
+}
diff --git a/man/prior_weightfunction.Rd b/man/prior_weightfunction.Rd
index 653630e7..c524feb1 100644
--- a/man/prior_weightfunction.Rd
+++ b/man/prior_weightfunction.Rd
@@ -42,6 +42,14 @@ its prior distributions.}
 \code{prior_weightfunction} creates a prior distribution for fitting
 a RoBMA selection model. The prior can be visualized by the \code{plot} function.
 }
+\details{
+Constrained cases of weight functions can be specified by adding
+".fixed" after the distribution name, i.e., \code{"two.sided.fixed"} and
+\code{"one.sided.fixed"}. In these cases, the functions are specified using
+\code{steps} and \code{omega} parameters, where the \code{omega} parameter
+is a vector of weights that corresponds to the relative publication probability
+(i.e., no parameters are estimated).
+}
 \examples{
 p1 <- prior_weightfunction("one-sided", parameters = list(steps = c(.05, .10), alpha = c(1, 1, 1)))
 
diff --git a/man/update.BayesTools_table.Rd b/man/update.BayesTools_table.Rd
new file mode 100644
index 00000000..9868e8f1
--- /dev/null
+++ b/man/update.BayesTools_table.Rd
@@ -0,0 +1,40 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/summary-tables.R
+\name{update.BayesTools_table}
+\alias{update.BayesTools_table}
+\title{Updates BayesTools table}
+\usage{
+\method{update}{BayesTools_table}(
+  object,
+  title = NULL,
+  footnotes = NULL,
+  warnings = NULL,
+  remove_parameters = NULL,
+  logBF = FALSE,
+  BF01 = FALSE,
+  ...
+)
+}
+\arguments{
+\item{object}{a BayesTools table}
+
+\item{title}{title of the table}
+
+\item{footnotes}{add footnotes to the table}
+
+\item{warnings}{add warnings of the table}
+
+\item{remove_parameters}{remove parameters from the table}
+
+\item{logBF}{whether to format Bayes factors as log(BF)}
+
+\item{BF01}{whether to format Bayes factors as 1/BF}
+
+\item{...}{additional arguments.}
+}
+\value{
+returns an object of 'BayesTools_table' class.
+}
+\description{
+Updates BayesTools table while not breaking formatting, attributes, etc...
+}
diff --git a/man/weightfunctions_mapping.Rd b/man/weightfunctions_mapping.Rd
index a3c7acd9..d7087ebc 100644
--- a/man/weightfunctions_mapping.Rd
+++ b/man/weightfunctions_mapping.Rd
@@ -4,12 +4,14 @@
 \alias{weightfunctions_mapping}
 \title{Create coefficient mapping between multiple weightfunctions}
 \usage{
-weightfunctions_mapping(prior_list, cuts_only = FALSE)
+weightfunctions_mapping(prior_list, cuts_only = FALSE, one_sided = FALSE)
 }
 \arguments{
 \item{prior_list}{list of prior distributions}
 
 \item{cuts_only}{whether only p-value cuts should be returned}
+
+\item{one_sided}{force one-sided output}
 }
 \value{
 \code{weightfunctions_mapping} returns a list of indices
diff --git a/tests/testthat/_snaps/JAGS-diagnostics/diagnostics-ggplot-density-1.svg b/tests/testthat/_snaps/JAGS-diagnostics/diagnostics-ggplot-density-1.svg
index 09d4ba1b..ed99d457 100644
--- a/tests/testthat/_snaps/JAGS-diagnostics/diagnostics-ggplot-density-1.svg
+++ b/tests/testthat/_snaps/JAGS-diagnostics/diagnostics-ggplot-density-1.svg
@@ -47,10 +47,10 @@
 <polyline points='433.54,544.93 433.54,24.63 ' style='stroke-width: 1.07; stroke: #FFFFFF; stroke-linecap: butt;' />
 <polyline points='558.42,544.93 558.42,24.63 ' style='stroke-width: 1.07; stroke: #FFFFFF; stroke-linecap: butt;' />
 <polyline points='683.30,544.93 683.30,24.63 ' style='stroke-width: 1.07; stroke: #FFFFFF; stroke-linecap: butt;' />
-<polyline points='104.44,521.01 105.01,520.99 105.59,520.96 106.16,520.94 106.74,520.92 107.32,520.89 107.89,520.87 108.47,520.84 109.04,520.82 109.62,520.79 110.19,520.76 110.77,520.73 111.34,520.70 111.92,520.67 112.50,520.64 113.07,520.60 113.65,520.57 114.22,520.54 114.80,520.50 115.37,520.46 115.95,520.43 116.52,520.39 117.10,520.35 117.68,520.31 118.25,520.27 118.83,520.23 119.40,520.19 119.98,520.14 120.55,520.10 121.13,520.05 121.70,520.00 122.28,519.95 122.86,519.90 123.43,519.85 124.01,519.80 124.58,519.74 125.16,519.69 125.73,519.63 126.31,519.57 126.88,519.50 127.46,519.44 128.04,519.37 128.61,519.30 129.19,519.23 129.76,519.15 130.34,519.07 130.91,518.99 131.49,518.90 132.06,518.81 132.64,518.72 133.22,518.63 133.79,518.53 134.37,518.42 134.94,518.31 135.52,518.20 136.09,518.08 136.67,517.96 137.24,517.83 137.82,517.70 138.40,517.56 138.97,517.41 139.55,517.26 140.12,517.10 140.70,516.94 141.27,516.76 141.85,516.58 142.42,516.40 143.00,516.21 143.58,516.01 144.15,515.80 144.73,515.58 145.30,515.36 145.88,515.13 146.45,514.89 147.03,514.65 147.60,514.39 148.18,514.14 148.76,513.87 149.33,513.59 149.91,513.31 150.48,513.02 151.06,512.73 151.63,512.43 152.21,512.13 152.78,511.82 153.36,511.50 153.94,511.18 154.51,510.86 155.09,510.53 155.66,510.20 156.24,509.87 156.81,509.53 157.39,509.19 157.96,508.85 158.54,508.51 159.12,508.16 159.69,507.82 160.27,507.47 160.84,507.12 161.42,506.78 161.99,506.43 162.57,506.08 163.14,505.73 163.72,505.38 164.30,505.03 164.87,504.68 165.45,504.33 166.02,503.98 166.60,503.63 167.17,503.28 167.75,502.92 168.32,502.57 168.90,502.21 169.47,501.85 170.05,501.49 170.63,501.12 171.20,500.75 171.78,500.38 172.35,500.00 172.93,499.61 173.50,499.23 174.08,498.83 174.65,498.43 175.23,498.03 175.81,497.61 176.38,497.19 176.96,496.76 177.53,496.33 178.11,495.88 178.68,495.42 179.26,494.96 179.83,494.48 180.41,493.99 180.99,493.49 181.56,492.99 182.14,492.46 182.71,491.93 183.29,491.38 183.86,490.82 184.44,490.25 185.01,489.66 185.59,489.06 186.17,488.45 186.74,487.82 187.32,487.17 187.89,486.51 188.47,485.84 189.04,485.15 189.62,484.44 190.19,483.72 190.77,482.99 191.35,482.24 191.92,481.47 192.50,480.69 193.07,479.89 193.65,479.08 194.22,478.25 194.80,477.41 195.37,476.55 195.95,475.68 196.53,474.79 197.10,473.89 197.68,472.97 198.25,472.04 198.83,471.10 199.40,470.14 199.98,469.16 200.55,468.17 201.13,467.17 201.71,466.15 202.28,465.11 202.86,464.06 203.43,463.00 204.01,461.92 204.58,460.82 205.16,459.71 205.73,458.59 206.31,457.44 206.89,456.28 207.46,455.10 208.04,453.91 208.61,452.70 209.19,451.47 209.76,450.22 210.34,448.96 210.91,447.67 211.49,446.37 212.07,445.05 212.64,443.72 213.22,442.36 213.79,440.99 214.37,439.61 214.94,438.20 215.52,436.78 216.09,435.35 216.67,433.89 217.25,432.43 217.82,430.94 218.40,429.45 218.97,427.94 219.55,426.43 220.12,424.90 220.70,423.35 221.27,421.80 221.85,420.24 222.43,418.67 223.00,417.10 223.58,415.51 224.15,413.92 224.73,412.33 225.30,410.72 225.88,409.11 226.45,407.49 227.03,405.87 227.61,404.24 228.18,402.60 228.76,400.96 229.33,399.31 229.91,397.65 230.48,395.98 231.06,394.30 231.63,392.62 232.21,390.91 232.79,389.20 233.36,387.47 233.94,385.73 234.51,383.97 235.09,382.19 235.66,380.39 236.24,378.57 236.81,376.73 237.39,374.87 237.97,372.99 238.54,371.08 239.12,369.14 239.69,367.18 240.27,365.18 240.84,363.16 241.42,361.12 241.99,359.04 242.57,356.94 243.14,354.80 243.72,352.64 244.30,350.45 244.87,348.23 245.45,345.98 246.02,343.71 246.60,341.41 247.17,339.09 247.75,336.74 248.32,334.37 248.90,331.98 249.48,329.57 250.05,327.14 250.63,324.69 251.20,322.23 251.78,319.75 252.35,317.27 252.93,314.77 253.50,312.26 254.08,309.75 254.66,307.23 255.23,304.71 255.81,302.19 256.38,299.67 256.96,297.15 257.53,294.63 258.11,292.12 258.68,289.61 259.26,287.11 259.84,284.61 260.41,282.13 260.99,279.65 261.56,277.19 262.14,274.73 262.71,272.29 263.29,269.86 263.86,267.45 264.44,265.04 265.02,262.66 265.59,260.29 266.17,257.93 266.74,255.59 267.32,253.27 267.89,250.96 268.47,248.67 269.04,246.40 269.62,244.15 270.20,241.91 270.77,239.70 271.35,237.50 271.92,235.32 272.50,233.17 273.07,231.03 273.65,228.92 274.22,226.82 274.80,224.75 275.38,222.71 275.95,220.68 276.53,218.68 277.10,216.70 277.68,214.75 278.25,212.83 278.83,210.93 279.40,209.06 279.98,207.21 280.56,205.39 281.13,203.60 281.71,201.84 282.28,200.10 282.86,198.40 283.43,196.72 284.01,195.08 284.58,193.46 285.16,191.87 285.74,190.31 286.31,188.78 286.89,187.27 287.46,185.80 288.04,184.36 288.61,182.94 289.19,181.55 289.76,180.18 290.34,178.84 290.92,177.53 291.49,176.23 292.07,174.97 292.64,173.72 293.22,172.50 293.79,171.29 294.37,170.10 294.94,168.93 295.52,167.78 296.10,166.63 296.67,165.51 297.25,164.39 297.82,163.29 298.40,162.19 298.97,161.10 299.55,160.01 300.12,158.93 300.70,157.85 301.28,156.77 301.85,155.69 302.43,154.61 303.00,153.52 303.58,152.43 304.15,151.34 304.73,150.23 305.30,149.12 305.88,148.00 306.46,146.87 307.03,145.73 307.61,144.57 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index 9c35418f..01b55eca 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-con-p-exp.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-con-p-exp.svg
index 1f6236fe..47794cdd 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-con.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-con.svg
index 235c57ce..f3641cdc 100644
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+++ b/tests/testthat/_snaps/marginal-distributions/marginal-form-con.svg
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-at.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md-at.svg
index bdbcdf2b..1bfd629c 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-md.svg
index c8e16c44..fa73ae0e 100644
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index 9d361901..6d38ffab 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-fac-t.svg
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-form-int.svg b/tests/testthat/_snaps/marginal-distributions/marginal-form-int.svg
index 8236ea72..e0dbc187 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont-p.svg
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-cont.svg
index b49a3859..71ac0c4c 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md-p.svg
index ba9aba18..b9ef91d0 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md.svg b/tests/testthat/_snaps/marginal-distributions/marginal-inference-fac-md.svg
index 96ee1824..788ea144 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac.svg b/tests/testthat/_snaps/marginal-distributions/marginal-simple-fac.svg
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new file mode 100644
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diff --git a/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t-p.svg b/tests/testthat/_snaps/marginal-distributions/marginal-ss-form-fac-t-p.svg
new file mode 100644
index 00000000..9d8188f4
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diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-md-5.svg b/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-md-5.svg
index f32ba970..50a955dd 100644
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diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-o-5.svg b/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-o-5.svg
index bf4052ad..9cc62e7a 100644
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index b51f5abd..5752b611 100644
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diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-simple-6.svg b/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-simple-6.svg
index 40e1eb2b..676e924a 100644
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diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-t-1.svg b/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-t-1.svg
index 425fb9d1..f453553e 100644
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diff --git a/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-t-5.svg b/tests/testthat/_snaps/model-averaging-plots/model-averaging-plot-posterior-t-5.svg
index af9c88ec..660b6969 100644
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index 55476960..01a17dc7 100644
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diff --git a/tests/testthat/_snaps/priors/prior-weigthfunction-one-sided-5.svg b/tests/testthat/_snaps/priors/prior-weigthfunction-one-sided-5.svg
index c5ca87e1..d419df65 100644
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index 7fedc4b5..69e0aa55 100644
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diff --git a/tests/testthat/test-JAGS-fit.R b/tests/testthat/test-JAGS-fit.R
index 0483974e..f60dde9c 100644
--- a/tests/testthat/test-JAGS-fit.R
+++ b/tests/testthat/test-JAGS-fit.R
@@ -364,6 +364,103 @@ test_that("JAGS model functions work (spike and slab)", {
   }
 })
 
+test_that("JAGS model functions work (mixture)", {
+
+  skip_if_not_installed("rjags")
+  priors       <- list(
+    "mu" = prior_mixture(
+      list(
+        prior("normal", list(0,  1), prior_weights = 1),
+        prior("normal", list(-3, 1), prior_weights = 5),
+        prior("gamma",  list(5, 10), prior_weights = 1)
+      ),
+      is_null = c(T, F, T)
+    ),
+    "beta" = prior_mixture(
+      list(
+        prior("normal", list(0,  1), prior_weights = 1),
+        prior("normal", list(-3, 1), prior_weights = 5)
+      ),
+      components = c("b", "a")
+    ),
+    "gamma" = prior_mixture(
+      list(
+        prior("spike", list(2)),
+        prior("normal", list(-3, 1))
+      )
+    ),
+    "bias" = prior_mixture(list(
+      prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+      prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/12),
+      prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/12),
+      prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12),
+      prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12),
+      prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12),
+      prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4),
+      prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4)
+    ))
+  )
+
+
+
+  for(i in 1:length(priors)){
+    model_syntax <- "model{}"
+    model_syntax <- JAGS_add_priors(model_syntax, priors[i])
+    monitor      <- JAGS_to_monitor(priors[i])
+    inits        <- JAGS_get_inits(priors[i], chains = 2, seed = 1)
+
+    if(i == 4){
+      if("RoBMA" %in% rownames(installed.packages())){
+        require("RoBMA")
+      }else{
+        next
+      }
+    }
+
+    set.seed(1)
+    model   <- rjags::jags.model(file = textConnection(model_syntax), inits = inits, n.chains = 2, quiet = TRUE)
+    samples <- rjags::coda.samples(model = model, variable.names = monitor, n.iter = 5000, quiet = TRUE, progress.bar = "none")
+    samples <- do.call(rbind, samples)
+
+
+    if(i != 4){
+      vdiffr::expect_doppelganger(paste0("JAGS-model-prior_mixture-",i), function(){
+        temp_samples <- samples[,names(priors)[i]]
+        hist(temp_samples, breaks = 100, freq = FALSE, main = print(priors[[i]], plot = TRUE))
+        lines(density(rng(priors[[i]], 1000000)))
+      })
+    }else{
+      vdiffr::expect_doppelganger(paste0("JAGS-model-prior_mixture-",i), function(){
+
+        oldpar <- graphics::par(no.readonly = TRUE)
+        on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+        par(mfrow = c(3, 3))
+
+        samples_PET   <- samples[,"PET"]
+        samples_PEESE <- samples[,"PEESE"]
+        samples_omega <- samples[,paste0("omega[",1:6,"]")]
+        samples_bias  <- samples[,"bias_indicator"]
+
+        barplot(table(samples_bias)/length(samples_bias), main = "Bias componenets")
+
+        hist(samples_PET[samples_PET != 0 & samples_PET < 10], breaks = 50, main = "PET", freq = FALSE)
+        lines(priors$bias[[7]], individual = TRUE)
+
+        hist(samples_PEESE[samples_PEESE != 0 & samples_PEESE < 20], breaks = 50, main = "PEESE", freq = FALSE)
+        lines(priors$bias[[8]], individual = TRUE)
+
+        hist(samples_omega[samples_bias == 2, 1], breaks = 50, main = "omega[2:1]", freq = FALSE)
+        hist(samples_omega[samples_bias == 2, 2], breaks = 50, main = "omega[2:2]", freq = FALSE)
+        hist(samples_omega[samples_bias == 2, 3], breaks = 50, main = "omega[2:3]", freq = FALSE)
+        hist(samples_omega[samples_bias == 2, 4], breaks = 50, main = "omega[2:4]", freq = FALSE)
+        hist(samples_omega[samples_bias == 2, 5], breaks = 50, main = "omega[2:5]", freq = FALSE)
+        hist(samples_omega[samples_bias == 2, 6], breaks = 50, main = "omega[2:6]", freq = FALSE)
+
+        })
+    }
+  }
+})
+
 test_that("JAGS fit function works" , {
 
   set.seed(1)
@@ -446,10 +543,10 @@ test_that("JAGS fit function works" , {
 
   convergence <- JAGS_check_convergence(fit4, prior_list = priors_list4, max_Rhat = 1.001, min_ESS = 500, max_error = 0.01, max_SD_error = 0.05)
   expect_true(!convergence)
-  expect_equal(attr(convergence, "errors")[1], "R-hat 1.007 is larger than the set target (1.001).")
+  expect_equal(attr(convergence, "errors")[1], "R-hat 1.053 is larger than the set target (1.001).")
   expect_equal(attr(convergence, "errors")[2], "ESS 149 is lower than the set target (500).")
-  expect_equal(attr(convergence, "errors")[3], "MCMC error 0.0698 is larger than the set target (0.01).")
-  expect_equal(attr(convergence, "errors")[4], "MCMC SD error 0.082 is larger than the set target (0.05).")
+  expect_equal(attr(convergence, "errors")[3], "MCMC error 0.07422 is larger than the set target (0.01).")
+  expect_equal(attr(convergence, "errors")[4], "MCMC SD error 0.087 is larger than the set target (0.05).")
 
   fit4b <- JAGS_fit(model_syntax4, data4, priors_list4, autofit = TRUE, autofit_control = list(max_error = 0.05, sample_extend = 100),
                     chains = 2, adapt = 100, burnin = 50, sample = 100, seed = 4)
@@ -703,6 +800,110 @@ test_that("JAGS fit function integration with formula and spike and slab works"
 
 })
 
+test_that("JAGS fit function integration with formula, spike and slab works, and mixture works" , {
+
+  skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes
+  skip_on_cran()
+
+  set.seed(1)
+
+  data_formula <- data.frame(
+    x_cont1 = rnorm(300),
+    x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")),
+    x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C"))
+  )
+  data <- list(
+    y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)),
+    N = 300
+  )
+
+  # create model with mix of a formula and free parameters ---
+  formula_list1 <- list(
+    mu    = ~ x_cont1 + x_fac3t
+  )
+  formula_data_list1 <- list(
+    mu    = data_formula
+  )
+  formula_prior_list1 <- list(
+    mu    = list(
+      "intercept"  = prior_mixture(
+        list(
+          prior("spike",   list(0),       prior_weights = 2),
+          prior("normal",  list(-1, 0.5), prior_weights = 1),
+          prior("normal",  list( 1, 0.5), prior_weights = 1)
+        ),
+        is_null = c(T, F, F)
+      ),
+      "x_cont1"    = prior_mixture(
+        list(
+          prior("spike",   list(0),    prior_weights = 1),
+          prior("normal",  list(0, 1), prior_weights = 1)
+        ),
+        is_null = c(T, F)
+      ),
+      "x_fac3t"    = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"),
+                                          prior_inclusion = prior("spike", list(0.5)))
+    )
+  )
+  attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma"
+  prior_list1 <- list(
+    "sigma" = prior_mixture(
+      list(
+        prior("normal",    list(0, 1), truncation = list(0, Inf)),
+        prior("lognormal", list(0, 1))
+      ),
+      is_null = c(T, F)
+    )
+  )
+  model_syntax1 <- paste0(
+    "model{\n",
+    "for(i in 1:N){\n",
+    "  y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n",
+    "}\n",
+    "}"
+  )
+
+  fit1 <- JAGS_fit(
+    model_syntax = model_syntax1, data = data, prior_list = prior_list1,
+    formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1)
+
+  posterior1 <- suppressWarnings(coda::as.mcmc(fit1))
+
+  vdiffr::expect_doppelganger("JAGS-fit-formula-mixture-1", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfcol = oldpar[["mfcol"]]))
+    par(mfrow = c(3, 3))
+
+    barplot(table(posterior1[,"mu_intercept_indicator"]) / nrow(posterior1), main = "Intercept indicator")
+    barplot(table(posterior1[,"mu_x_cont1_indicator"]) / nrow(posterior1), main = "x_cont1 indicator")
+    barplot(table(posterior1[,"sigma_indicator"]) / nrow(posterior1), main = "sigma indicator")
+
+    hist(posterior1[,"mu_intercept"], freq = FALSE, main = "mu_intercept")
+    hist(posterior1[,"mu_x_cont1"], freq = FALSE, main = "x_cont1")
+    hist(posterior1[,"sigma"], freq = FALSE, main = "sigma")
+  })
+
+  vdiffr::expect_doppelganger("JAGS-fit-formula-mixture-2", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfcol = oldpar[["mfcol"]]))
+    par(mfrow = c(2, 3))
+
+    temp_samples          <- posterior1[,paste0("mu_x_fac3t[", 1:2, "]")]          %*% t(contr.orthonormal(1:3))
+    temp_samples_variable <- posterior1[,paste0("mu_x_fac3t_variable[", 1:2, "]")]  %*% t(contr.orthonormal(1:3))
+
+    hist(temp_samples[,1], freq = FALSE, main = "x_fac3t[A]")
+    hist(temp_samples[,2], freq = FALSE, main = "x_fac3t[B]")
+    hist(temp_samples[,3], freq = FALSE, main = "x_fac3t[C]")
+
+    hist(temp_samples_variable[,1], freq = FALSE, main = "x_fac3t_variable[A]")
+    hist(temp_samples_variable[,2], freq = FALSE, main = "x_fac3t_variable[B]")
+    hist(temp_samples_variable[,3], freq = FALSE, main = "x_fac3t_variable[C]")
+  })
+
+})
+
 test_that("JAGS parallel fit function works" , {
 
   skip("requires parallel processing")
diff --git a/tests/testthat/test-marginal-distributions.R b/tests/testthat/test-marginal-distributions.R
index 22d52c0b..cac2232f 100644
--- a/tests/testthat/test-marginal-distributions.R
+++ b/tests/testthat/test-marginal-distributions.R
@@ -590,7 +590,7 @@ test_that("Marginal distribution prior and posterior functions work", {
 
   # simple factor
   BF.marg_post_x_fac2t <- suppressWarnings(Savage_Dickey_BF(marg_post_simple_x_fac2t))
-  expect_equivalent(BF.marg_post_x_fac2t, list("A" = 1, "B" = 1.660247), tolerance = 1e-5)
+  expect_equivalent(BF.marg_post_x_fac2t, list("A" = 1, "B" = 1.660692), tolerance = 1e-3)
   expect_equal(attr(BF.marg_post_x_fac2t[["A"]], "warnings"),
                c("There is a considerable cluster of posterior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid.",
                  "There is a considerable cluster of prior samples at the exact null hypothesis values. The Savage-Dickey density ratio is likely to be invalid."))
@@ -600,10 +600,10 @@ test_that("Marginal distribution prior and posterior functions work", {
   expect_equivalent(BF.marg_post_x_fac3md, list("A" = Inf, "B" = Inf, "C" = Inf))
 
   BF2.marg_post_x_fac3md <- Savage_Dickey_BF(marg_post_x_fac3md, null_hypothesis = 0.5)
-  expect_equivalent(BF2.marg_post_x_fac3md, list("A" = 3.950433, "B" = 0.1405758, "C" = 0.1661129), tolerance = 1e-5)
+  expect_equivalent(BF2.marg_post_x_fac3md, list("A" = 3.954431, "B" = 0.1405823, "C" = 0.1661251), tolerance = 1e-3)
 
   BF2.marg_post_x_fac3md <- Savage_Dickey_BF(marg_post_x_fac3md, null_hypothesis = 0.5, normal_approximation = TRUE)
-  expect_equal(BF2.marg_post_x_fac3md, list("A" = 0.6342651, "B" = 0.1015235, "C" = 0.1267758), tolerance = 1e-5)
+  expect_equal(BF2.marg_post_x_fac3md, list("A" = 0.6342651, "B" = 0.1015235, "C" = 0.1267758), tolerance = 1e-3)
 
   ### marginal_inference ----
   out <- marginal_inference(
@@ -699,10 +699,10 @@ test_that("Marginal distribution prior and posterior functions work", {
        "(mu) x_cont1:x_fac3md[-1SD, A] 0.556  0.556 0.344 0.734          Inf"                                                                                                               ,
        "(mu) x_cont1:x_fac3md[0SD, A]  0.770  0.772 0.618 0.893          Inf"                                                                                                               ,
        "(mu) x_cont1:x_fac3md[1SD, A]  0.984  0.985 0.791 1.140          Inf"                                                                                                               ,
-       "(mu) x_cont1:x_fac3md[-1SD, B] 0.372  0.372 0.159 0.556       10.812"                                                                                                               ,
+       "(mu) x_cont1:x_fac3md[-1SD, B] 0.372  0.372 0.159 0.556       10.816"                                                                                                               ,
        "(mu) x_cont1:x_fac3md[0SD, B]  0.518  0.518 0.365 0.646          Inf"                                                                                                               ,
        "(mu) x_cont1:x_fac3md[1SD, B]  0.665  0.664 0.464 0.830          Inf"                                                                                                               ,
-       "(mu) x_cont1:x_fac3md[-1SD, C] 0.373  0.373 0.171 0.541       69.936"                                                                                                               ,
+       "(mu) x_cont1:x_fac3md[-1SD, C] 0.373  0.373 0.171 0.541       69.939"                                                                                                               ,
        "(mu) x_cont1:x_fac3md[0SD, C]  0.550  0.551 0.405 0.674          Inf"                                                                                                               ,
        "(mu) x_cont1:x_fac3md[1SD, C]  0.727  0.727 0.524 0.904          Inf"                                                                                                               ,
        "\033[0;31mmu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                     ,
@@ -817,3 +817,527 @@ test_that("Marginal distribution prior functions work", {
   })
 
 })
+
+test_that("Marginal distributions with spike and slab and mixture priors work", {
+
+  skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes
+  skip_on_cran()
+
+  ### complex formula including scaling ----
+  set.seed(1)
+  df_all <- data.frame(
+    x_cont1  = rnorm(180),
+    x_fac2t  = factor(rep(c("A", "B"), 90), levels = c("A", "B")),
+    x_fac3md = factor(rep(c("A", "B", "C"), 60), levels = c("A", "B", "C"))
+  )
+  df_all$y <- rnorm(180, 0.1, 0.5) + 0.5 + 0.20 * df_all$x_cont1 +
+    ifelse(df_all$x_fac3md == "A", 0.15, ifelse(df_all$x_fac3md == "B", -0.15, 0))
+
+  prior_pars <- list(
+    "intercept"        = prior("normal", list(0, 1)),
+    "x_cont1"          = prior_mixture(list(
+      prior("spike", list(0)),
+      prior("normal", list(0, 1))
+    ), is_null = c(T, F)),
+    "x_fac2t"          = prior_spike_and_slab(prior_factor("normal",  contrast = "treatment", list(0, 1.00))),
+    "x_fac3md"         = prior_spike_and_slab(prior_factor("mnormal", contrast = "meandif",   list(0, 0.25))),
+    "x_cont1:x_fac3md" = prior_spike_and_slab(prior_factor("mnormal", contrast = "meandif",   list(0, 0.25)))
+  )
+  prior_list <- list(
+    "sigma" = prior("cauchy", list(0, 1), list(0, 5))
+  )
+  attr(prior_pars$x_cont1, "multiply_by") <- "sigma"
+  model_syntax <- paste0(
+    "model{",
+    "for(i in 1:N){\n",
+    "  y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n",
+    "}\n",
+    "}"
+  )
+
+  model_formula <- list(mu = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md)
+
+  fit <- JAGS_fit(
+    model_syntax = model_syntax, data = list(y = df_all$y, N = nrow(df_all)),
+    prior_list = prior_list,
+    formula_list       = model_formula,
+    formula_prior_list = list(mu = prior_pars),
+    formula_data_list  = list(mu = df_all))
+
+  mixed_posteriors <- as_mixed_posteriors(
+    model        = fit,
+    parameters   = c("sigma", "mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md")
+  )
+
+  ### simple: continuous parameter ----
+  marg_post_sigma <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "sigma",
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-simple-con", function(){
+    hist(marg_post_sigma, freq = FALSE, main = "marginal posterior sigma")
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-simple-con-p", function(){
+    hist(attr(marg_post_sigma, "prior_samples"), freq = FALSE, main = "marginal prior sigma", breaks = 20)
+    lines(density(prior_list$sigma))
+  })
+
+
+  ### simple: factor ----
+  marg_post_simple_x_fac2t <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_fac2t",
+    prior_samples     = TRUE,
+    use_formula       = FALSE)
+
+  vdiffr::expect_doppelganger("marginal-ss-simple-fac", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 2))
+    hist(marg_post_simple_x_fac2t[["A"]], freq = FALSE, main = "marg_post_x_fac2t = A")
+
+    hist(marg_post_simple_x_fac2t[["B"]], freq = FALSE, main = "marg_post_x_fac2t = B", breaks = 20)
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-simple-fac-p", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 2))
+    hist(attr(marg_post_simple_x_fac2t[["A"]], "prior_samples"), freq = FALSE, main = "marg_post_x_fac2t = A", breaks = 20)
+
+    hist(attr(marg_post_simple_x_fac2t[["B"]], "prior_samples"), freq = FALSE, main = "marg_post_x_fac2t = B", breaks = 20)
+    curve(dnorm(x, 0, 1)/2, add = T)
+
+  })
+
+
+  ### formula: intercept ----
+  marg_post_int <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_intercept",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-form-int", function(){
+    hist(marg_post_int[["intercept"]], freq = FALSE, main = "marginal posterior intercept")
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-form-int-p", function(){
+    hist(attr(marg_post_int[["intercept"]], "prior_samples"), freq = FALSE, main = "marginal prior intercept")
+    lines(prior_pars$intercept)
+  })
+
+
+  ### formula: continuous parameter (-+1SD) ----
+  marg_post_x_cont1 <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_cont1",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-form-con", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(marg_post_x_cont1[["-1SD"]], freq = FALSE, main = "marginal posterior x_cont1\n(-1)")
+    hist(marg_post_x_cont1[["0SD"]], freq = FALSE, main = "marginal posterior x_cont1\n(0)")
+    hist(marg_post_x_cont1[["1SD"]], freq = FALSE, main = "marginal posterior x_cont1\n(1)")
+
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-form-con-p", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(attr(marg_post_x_cont1[["-1SD"]], "prior_samples"), freq = FALSE, main = "marginal prior x_cont1\n(-1)", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-10, 10))
+    hist(attr(marg_post_x_cont1[["0SD"]], "prior_samples"),  freq = FALSE, main = "marginal prior x_cont1\n(0)",  breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-10, 10))
+    hist(attr(marg_post_x_cont1[["1SD"]], "prior_samples"),  freq = FALSE, main = "marginal prior x_cont1\n(1)",  breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-10, 10))
+
+  })
+
+
+  ### formula: treatment factor ----
+  marg_post_x_fac2t <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_fac2t",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.t", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 2))
+    hist(marg_post_x_fac2t[["A"]], freq = FALSE, main = "marg_post_x_fac2t = A")
+    hist(marg_post_x_fac2t[["B"]], freq = FALSE, main = "marg_post_x_fac2t = B", breaks = 20)
+
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.t-p", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 2))
+    hist(attr(marg_post_x_fac2t[["A"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac2t = A", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x), add = TRUE)
+    hist(attr(marg_post_x_fac2t[["B"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac2t = B",  breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, (sqrt(1^2 + 0^2) + sqrt(1^2 + 1^2)) / 2), add = TRUE)
+  })
+
+
+  ### formula: meandif factor ----
+  marg_post_x_fac3md <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_fac3md",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.md", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(marg_post_x_fac3md[["A"]], freq = FALSE, main = "marg_post_x_fac3md = A", breaks = 20)
+    hist(marg_post_x_fac3md[["B"]], freq = FALSE, main = "marg_post_x_fac3md = B", breaks = 20)
+    hist(marg_post_x_fac3md[["C"]], freq = FALSE, main = "marg_post_x_fac2t = B", breaks = 20)
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.md-p", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(attr(marg_post_x_fac3md[["A"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac3md = A", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, (sqrt(1^2 + 0^2) + sqrt(1^2 + 0.25^2)) / 2), add = TRUE)
+    hist(attr(marg_post_x_fac3md[["B"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, (sqrt(1^2 + 0^2) + sqrt(1^2 + 0.25^2)) / 2), add = TRUE)
+    hist(attr(marg_post_x_fac3md[["C"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac3md = C", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, (sqrt(1^2 + 0^2) + sqrt(1^2 + 0.25^2)) / 2), add = TRUE)
+  })
+
+
+  ### formula: meandif factor interaction ----
+  marg_post_x_cont1__xXx__x_fac3md <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_cont1__xXx__x_fac3md",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.mdi", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(3, 3))
+    hist(marg_post_x_cont1__xXx__x_fac3md[["-1SD, A"]], freq = FALSE, main = "x_cont1 = -1\nmarg_post_x_fac3md = A", breaks = 20)
+    hist(marg_post_x_cont1__xXx__x_fac3md[["-1SD, B"]], freq = FALSE, main = "x_cont1 = -1\nmarg_post_x_fac3md = B", breaks = 20)
+    hist(marg_post_x_cont1__xXx__x_fac3md[["-1SD, C"]], freq = FALSE, main = "x_cont1 = -1\nmarg_post_x_fac3md = B", breaks = 20)
+
+    hist(marg_post_x_cont1__xXx__x_fac3md[["0SD, A"]], freq = FALSE, main = "x_cont1 = 0\nmarg_post_x_fac3md = A", breaks = 20)
+    hist(marg_post_x_cont1__xXx__x_fac3md[["0SD, B"]], freq = FALSE, main = "x_cont1 = 0\nmarg_post_x_fac3md = B", breaks = 20)
+    hist(marg_post_x_cont1__xXx__x_fac3md[["0SD, C"]], freq = FALSE, main = "x_cont1 = 0\nmarg_post_x_fac3md = B", breaks = 20)
+
+    hist(marg_post_x_cont1__xXx__x_fac3md[["1SD, A"]], freq = FALSE, main = "x_cont1 = 1\nmarg_post_x_fac3md = A", breaks = 20)
+    hist(marg_post_x_cont1__xXx__x_fac3md[["1SD, B"]], freq = FALSE, main = "x_cont1 = 1\nmarg_post_x_fac3md = B", breaks = 20)
+    hist(marg_post_x_cont1__xXx__x_fac3md[["1SD, C"]], freq = FALSE, main = "x_cont1 = 1\nmarg_post_x_fac3md = B", breaks = 20)
+
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.mdi-p", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(3, 3))
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["-1SD, A"]], "prior_samples"), freq = FALSE, main = "x_cont1 = -1\nmarg_post_x_fac3md = A", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(0.5*sqrt(1^2 + 1^2 + 0.25^2 + 0.25^2)^2 + 0.5*sqrt(0.25^2 + 0.25^2))), add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["-1SD, B"]], "prior_samples"), freq = FALSE, main = "x_cont1 = -1\nmarg_post_x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(0.5*sqrt(1^2 + 1^2 + 0.25^2 + 0.25^2)^2 + 0.5*sqrt(0.25^2 + 0.25^2))), add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["-1SD, C"]], "prior_samples"), freq = FALSE, main = "x_cont1 = -1\nmarg_post_x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(0.5*sqrt(1^2 + 1^2 + 0.25^2 + 0.25^2)^2 + 0.5*sqrt(0.25^2 + 0.25^2))), add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["0SD, A"]], "prior_samples"), freq = FALSE, main = "x_cont1 = 0\nmarg_post_x_fac3md = A", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(1^2 + 0 + 0.25^2 + 0)) , add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["0SD, B"]], "prior_samples"), freq = FALSE, main = "x_cont1 = 0\nmarg_post_x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(1^2 + 0 + 0.25^2 + 0)) , add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["0SD, C"]], "prior_samples"), freq = FALSE, main = "x_cont1 = 0\nmarg_post_x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(1^2 + 0 + 0.25^2 + 0)) , add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["1SD, A"]], "prior_samples"), freq = FALSE, main = "x_cont1 = 1\nmarg_post_x_fac3md = A", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(0.5*sqrt(1^2 + 1^2 + 0.25^2 + 0.25^2)^2 + 0.5*sqrt(0.25^2 + 0.25^2))), add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["1SD, B"]], "prior_samples"), freq = FALSE, main = "x_cont1 = 1\nmarg_post_x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(0.5*sqrt(1^2 + 1^2 + 0.25^2 + 0.25^2)^2 + 0.5*sqrt(0.25^2 + 0.25^2))), add = TRUE)
+
+    hist(attr(marg_post_x_cont1__xXx__x_fac3md[["1SD, C"]], "prior_samples"), freq = FALSE, main = "x_cont1 = 1\nmarg_post_x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    curve(dnorm(x, 0, sqrt(0.5*sqrt(1^2 + 1^2 + 0.25^2 + 0.25^2)^2 + 0.5*sqrt(0.25^2 + 0.25^2))), add = TRUE)
+
+  })
+
+
+  ### formula: meandif factor + at specification ----
+  marg_post_x_fac3md_AT <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_fac3md",
+    at                = list(
+      x_cont1 = 1,
+      x_fac2t = c("A", "B")
+    ),
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+
+  vdiffr::expect_doppelganger("marginal-ss-form-fac.md-at", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(3, 2))
+    hist(marg_post_x_fac3md_AT[["A"]][1,], freq = FALSE, main = "marg_post_x_fac3md = A | 1,A", breaks = 20)
+
+    hist(marg_post_x_fac3md_AT[["A"]][2,], freq = FALSE, main = "marg_post_x_fac3md = A | 1,B", breaks = 20)
+
+    hist(marg_post_x_fac3md_AT[["B"]][1,], freq = FALSE, main = "marg_post_x_fac3md = B | 1,A", breaks = 20)
+
+    hist(marg_post_x_fac3md_AT[["B"]][2,], freq = FALSE, main = "marg_post_x_fac3md = B | 1,B", breaks = 20)
+
+    hist(marg_post_x_fac3md_AT[["C"]][1,], freq = FALSE, main = "marg_post_x_fac3md = C | 1,A", breaks = 20)
+
+    hist(marg_post_x_fac3md_AT[["C"]][2,], freq = FALSE, main = "marg_post_x_fac3md = C | 1,B", breaks = 20)
+
+  })
+
+  ### formula: transformation ----
+  marg_post_x_cont1.exp <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_cont1",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    transformation    = "exp",
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-form-con-exp", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(marg_post_x_cont1.exp[["-1SD"]], freq = FALSE, main = "exp marginal posterior x_cont1\n(-1)")
+    lines(density(exp(marg_post_x_cont1[["-1SD"]])))
+
+    hist(marg_post_x_cont1.exp[["0SD"]], freq = FALSE, main = "exp marginal posterior x_cont1\n(0)")
+    lines(density(exp(marg_post_x_cont1[["0SD"]])))
+
+    hist(marg_post_x_cont1.exp[["1SD"]], freq = FALSE, main = "exp marginal posterior x_cont1\n(1)")
+    lines(density(exp(marg_post_x_cont1[["1SD"]])))
+
+  })
+
+  vdiffr::expect_doppelganger("marginal-ss-form-con-p-exp", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    p.exp1 <- exp(attr(marg_post_x_cont1[["-1SD"]], "prior_samples"))
+    p.exp2 <- exp(attr(marg_post_x_cont1[["0SD"]], "prior_samples"))
+    p.exp3 <- exp(attr(marg_post_x_cont1[["1SD"]], "prior_samples"))
+
+    par(mfrow = c(1, 3))
+    hist(attr(marg_post_x_cont1.exp[["-1SD"]], "prior_samples"), freq = FALSE, main = "marginal prior x_cont1\n(-1)", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-10, 10))
+    lines(density(p.exp1[p.exp1 < 10]))
+
+    hist(exp(attr(marg_post_x_cont1[["0SD"]], "prior_samples")),  freq = FALSE, main = "marginal prior x_cont1\n(0)",  breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-10, 10))
+    lines(density(p.exp2[p.exp2 < 10]))
+
+    hist(attr(marg_post_x_cont1.exp[["1SD"]], "prior_samples"),  freq = FALSE, main = "marginal prior x_cont1\n(1)",  breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-10, 10))
+    lines(density(p.exp3[p.exp3 < 10]))
+  })
+
+  ### conditional marginal samples ----
+  mixed_posteriors <- as_mixed_posteriors(
+    model        = fit,
+    parameters   = c("sigma", "mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md"),
+    conditional  = c("mu_x_cont1", "mu_x_fac3md"),
+    conditional_rule = "AND"
+  )
+  marg_post_sigma <- marginal_posterior(
+    samples           = mixed_posteriors,
+    parameter         = "mu_x_fac3md",
+    formula           = ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    prior_samples     = TRUE)
+
+  vdiffr::expect_doppelganger("marginal-ss-cond-fac", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(2, 3))
+    hist(marg_post_sigma[["A"]], freq = FALSE, main = "marg_post_x_fac2t = A")
+    hist(marg_post_sigma[["B"]], freq = FALSE, main = "marg_post_x_fac2t = B")
+    hist(marg_post_sigma[["C"]], freq = FALSE, main = "marg_post_x_fac2t = C")
+
+    hist(attr(marg_post_sigma[["A"]], "prior"), freq = FALSE, main = "marg_post_x_fac2t = A")
+    curve(dnorm(x, 0, sqrt(1^2 + 0.25^2)), add = TRUE)
+    hist(attr(marg_post_sigma[["B"]], "prior"), freq = FALSE, main = "marg_post_x_fac2t = B")
+    curve(dnorm(x, 0, sqrt(1^2 + 0.25^2)), add = TRUE)
+    hist(attr(marg_post_sigma[["C"]], "prior"), freq = FALSE, main = "marg_post_x_fac2t = C")
+    curve(dnorm(x, 0, sqrt(1^2 + 0.25^2)), add = TRUE)
+  })
+
+  ### marginal_inference ----
+  out <- as_marginal_inference(
+    model               = fit,
+    parameters          = c("sigma", "mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md"),
+    marginal_parameters = c("mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md"),
+    conditional_list    = list(
+      "mu_intercept"               = c(),
+      "mu_x_cont1"                 = c("mu_x_cont1"),
+      "mu_x_fac2t"                 = c("mu_x_cont1", "mu_x_fac2t"),
+      "mu_x_fac3md"                = c("mu_x_fac3md"),
+      "mu_x_cont1__xXx__x_fac3md"  = c("mu_x_fac2t", "mu_x_fac3md","mu_x_cont1__xXx__x_fac3md")
+    ),
+    conditional_rule    = "OR",
+    formula      =  ~ x_cont1 + x_fac2t + x_cont1*x_fac3md,
+    silent       = TRUE
+  )
+
+  # test samples against previously generated ones
+  vdiffr::expect_doppelganger("marginal_inference-ss-cont",     function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(marg_post_x_cont1[["-1SD"]], freq = FALSE, main = "mu_x_cont1 = -1SD", breaks = 20)
+    lines(density(out$averaged$mu_x_cont1[["-1SD"]]))
+
+    hist(marg_post_x_cont1[["0SD"]], freq = FALSE, main = "mu_x_cont1 = 0SD", breaks = 20)
+    lines(density(out$averaged$mu_x_cont1[["0SD"]]))
+
+    hist(marg_post_x_cont1[["1SD"]], freq = FALSE, main = "mu_x_cont1 = +1SD", breaks = 20)
+    lines(density(out$averaged$mu_x_cont1[["1SD"]]))
+
+  })
+  vdiffr::expect_doppelganger("marginal_inference-ss-cont-p",   function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(attr(marg_post_x_cont1[["-1SD"]], "prior_samples"), freq = FALSE, main = "mu_x_cont1 = -1SD", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    lines(density(attr(out$averaged$mu_x_cont1[["-1SD"]], "prior_samples")))
+    hist(attr(marg_post_x_cont1[["0SD"]], "prior_samples"), freq = FALSE, main = "mu_x_cont1 = 0SD", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    lines(density(attr(out$averaged$mu_x_cont1[["0SD"]], "prior_samples")))
+    hist(attr(marg_post_x_cont1[["1SD"]], "prior_samples"), freq = FALSE, main = "mu_x_cont1 = 1SD", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    lines(density(attr(out$averaged$mu_x_cont1[["1SD"]], "prior_samples")))
+  })
+  vdiffr::expect_doppelganger("marginal_inference-ss-fac.md",   function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(marg_post_x_fac3md[["A"]], freq = FALSE, main = "marg_post_x_fac3md = A", breaks = 20)
+    lines(density(out$averaged$mu_x_fac3md$A))
+
+    hist(marg_post_x_fac3md[["B"]], freq = FALSE, main = "marg_post_x_fac3md = B", breaks = 20)
+    lines(density(out$averaged$mu_x_fac3md$B))
+
+    hist(marg_post_x_fac3md[["C"]], freq = FALSE, main = "marg_post_x_fac3md = C", breaks = 20)
+    lines(density(out$averaged$mu_x_fac3md$C))
+
+  })
+  vdiffr::expect_doppelganger("marginal_inference-ss-fac.md-p", function(){
+
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mfrow = oldpar[["mfrow"]]))
+
+    par(mfrow = c(1, 3))
+    hist(attr(marg_post_x_fac3md[["A"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac3md = A", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    lines(density(attr(out$averaged$mu_x_fac3md$A, "prior_samples")))
+    hist(attr(marg_post_x_fac3md[["B"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac3md = B", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    lines(density(attr(out$averaged$mu_x_fac3md$B, "prior_samples")))
+    hist(attr(marg_post_x_fac3md[["C"]], "prior_samples"), freq = FALSE, main = "marginal prior x_fac3md = C", breaks = c(-Inf, seq(-10, 10, 0.25), Inf), xlim = c(-5, 5), ylim = c(0, 0.4))
+    lines(density(attr(out$averaged$mu_x_fac3md$C, "prior_samples")))
+  })
+  # the previous BFs were based on model-averaged posteriors so they won't match
+
+  # test summary table (note that these differ from the first set of tests because of the different model settings)
+  expect_equal(
+    capture_output_lines(marginal_estimates_table(out$conditional, out$inference, parameters = c("mu_intercept", "mu_x_cont1", "mu_x_fac2t", "mu_x_fac3md", "mu_x_cont1__xXx__x_fac3md")), print = TRUE, width = 150),
+    c("                                Mean Median 0.025  0.95 Inclusion BF"                                                                                                                ,
+      "(mu) intercept                 0.617  0.617 0.542 0.681          Inf"                                                                                                                ,
+      "(mu) x_cont1[-1SD]             0.435  0.434 0.320 0.531          Inf"                                                                                                                ,
+      "(mu) x_cont1[0SD]              0.617  0.617 0.542 0.681          Inf"                                                                                                                ,
+      "(mu) x_cont1[1SD]              0.800  0.799 0.691 0.890          Inf"                                                                                                                ,
+      "(mu) x_fac2t[A]                0.617  0.617 0.542 0.681          Inf"                                                                                                                ,
+      "(mu) x_fac2t[B]                0.618  0.617 0.542 0.682          Inf"                                                                                                                ,
+      "(mu) x_fac3md[A]               0.778  0.778 0.651 0.886          Inf"                                                                                                                ,
+      "(mu) x_fac3md[B]               0.518  0.518 0.390 0.625          Inf"                                                                                                                ,
+      "(mu) x_fac3md[C]               0.554  0.554 0.427 0.662          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[-1SD, A] 0.590  0.592 0.407 0.729          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[0SD, A]  0.774  0.776 0.623 0.884          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[1SD, A]  0.958  0.959 0.802 1.084          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[-1SD, B] 0.342  0.341 0.182 0.483      158.472"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[0SD, B]  0.521  0.520 0.392 0.631          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[1SD, B]  0.700  0.699 0.549 0.827          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[-1SD, C] 0.375  0.374 0.226 0.501          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[0SD, C]  0.556  0.556 0.428 0.663          Inf"                                                                                                                ,
+      "(mu) x_cont1:x_fac3md[1SD, C]  0.737  0.738 0.579 0.871          Inf"                                                                                                                ,
+      "\033[0;31mmu_intercept: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                      ,
+      "\033[0;31mmu_x_cont1[-1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                  ,
+      "\033[0;31mmu_x_cont1[0SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                   ,
+      "\033[0;31mmu_x_cont1[1SD]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                   ,
+      "\033[0;31mmu_x_fac2t[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                     ,
+      "\033[0;31mmu_x_fac2t[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                     ,
+      "\033[0;31mmu_x_fac3md[A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                    ,
+      "\033[0;31mmu_x_fac3md[B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                    ,
+      "\033[0;31mmu_x_fac3md[C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"                    ,
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[-1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m",
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" ,
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, A]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" ,
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" ,
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, B]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" ,
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[-1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m",
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[0SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m" ,
+      "\033[0;31mmu_x_cont1__xXx__x_fac3md[1SD, C]: Posterior samples do not span both sides of the null hypothesis. The Savage-Dickey density ratio is likely to be overestimated.\033[0m"
+    ))
+
+  # plots
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac2t-1", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t")})
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac2t-2", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t", par_name = "fac2t", lwd = 2)})
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac2t-3", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t", prior = TRUE, dots_prior = list(lty = 2))})
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac2t-4", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t", prior = TRUE, dots_prior = list(lty = 2), xlim = c(0, 1))})
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac2t-5", function(){plot_marginal(out$conditional, parameter = "mu_x_fac2t", prior = TRUE, dots_prior = list(lty = 2), transformation = "exp", xlim = c(0, 5), transformation_settings = T)})
+
+  vdiffr::expect_doppelganger("ggplot_marginal-ss-mu_x_fac2t-1", plot_marginal(out$conditional, plot_type = "ggplot", parameter = "mu_x_fac2t"))
+  vdiffr::expect_doppelganger("ggplot_marginal-ss-mu_x_fac2t-2", plot_marginal(out$conditional, plot_type = "ggplot", parameter = "mu_x_fac2t", par_name = "fac2t", lwd = 2))
+  vdiffr::expect_doppelganger("ggplot_marginal-ss-mu_x_fac2t-3", plot_marginal(out$conditional, plot_type = "ggplot", parameter = "mu_x_fac2t", prior = TRUE, dots_prior = list(lty = 2)))
+  vdiffr::expect_doppelganger("ggplot_marginal-ss-mu_x_fac2t-4", plot_marginal(out$conditional, plot_type = "ggplot", parameter = "mu_x_fac2t", prior = TRUE, dots_prior = list(lty = 2), xlim = c(0, 1)))
+
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_cont1", function(){plot_marginal(out$conditional, parameter = "mu_x_cont1", prior = TRUE, dots_prior = list(lty = 2), xlim = c(0, 1))})
+  vdiffr::expect_doppelganger("ggplot_marginal-ss-mu_x_cont1", plot_marginal(out$conditional, plot_type = "ggplot", parameter = "mu_x_cont1", prior = TRUE, dots_prior = list(lty = 2), xlim = c(0, 1)))
+
+  vdiffr::expect_doppelganger("plot_marginal-ss-mu_x_fac3md", function(){plot_marginal(out$averaged, parameter = "mu_x_fac3md", prior = TRUE, dots_prior = list(lty = 2), xlim = c(-1, 1))})
+  vdiffr::expect_doppelganger("ggplot_marginal-ss-mu_x_fac3md", plot_marginal(out$averaged, plot_type = "ggplot", parameter = "mu_x_fac3md", prior = TRUE, dots_prior = list(lty = 2), xlim = c(-1, 1)))
+
+  vdiffr::expect_doppelganger("plot_marginal-ss-int", plot_marginal(out$averaged, plot_type = "ggplot", parameter = "mu_intercept", prior = TRUE, dots_prior = list(lty = 2), xlim = c(-1, 1)))
+
+})
diff --git a/tests/testthat/test-model-averaging-plots.R b/tests/testthat/test-model-averaging-plots.R
index 4ff8441c..0e723930 100644
--- a/tests/testthat/test-model-averaging-plots.R
+++ b/tests/testthat/test-model-averaging-plots.R
@@ -1642,4 +1642,282 @@ test_that("models plot functions work (formulas + spike factors)", {
     plot_models(model_list = models, samples = mixed_posteriors, inference = inference, parameter = "mu_x_fac3md", prior = TRUE)
   })
 
+})
+
+
+test_that("posterior plot model averaging based on complex single JAGS models  (formulas + spike factors + mixture)", {
+
+  set.seed(1)
+
+  data_formula <- data.frame(
+    x_cont1 = rnorm(300),
+    x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")),
+    x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C"))
+  )
+  data <- list(
+    y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)),
+    N = 300
+  )
+
+  # create model with mix of a formula and free parameters ---
+  formula_list1 <- list(
+    mu    = ~ x_cont1 + x_fac2t + x_fac3t
+  )
+  formula_data_list1 <- list(
+    mu    = data_formula
+  )
+  formula_prior_list1 <- list(
+    mu    = list(
+      "intercept"  = prior_mixture(
+        list(
+          prior("spike",   list(0),       prior_weights = 2),
+          prior("normal",  list(-1, 0.5), prior_weights = 1),
+          prior("normal",  list( 1, 0.5), prior_weights = 1)
+        ),
+        is_null = c(T, F, F)
+      ),
+      "x_cont1"    = prior_spike_and_slab(prior("normal",  list(0, 1), prior_weights = 1)),
+      "x_fac2t"    = prior_mixture(list(
+          prior("spike", list(0), prior_weights = 1),
+          prior_factor("mnormal", list(0, 1), contrast = "orthonormal")
+        ),
+        is_null = c(T, F)
+      ),
+      "x_fac3t"    = prior_mixture(list(
+          prior("spike", list(0), prior_weights = 1),
+          prior_factor("mnormal", list(0, 1), contrast = "orthonormal")
+        ),
+        is_null = c(T, F)
+      )
+    )
+  )
+
+  attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma"
+  prior_list1 <- list(
+    "sigma" = prior_mixture(
+      list(
+        prior("normal",    list(0, 1), truncation = list(0, Inf)),
+        prior("lognormal", list(0, 1))
+      ),
+      components = c("normal", "lognormal")
+    ),
+    "bias"  = prior_mixture(list(
+      prior_none(prior_weights = 1),
+      prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/3),
+      prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/3),
+      prior_PET("normal", list(0, 1), prior_weights = 1/3)
+    ), is_null = c(T, F, F, F))
+  )
+  model_syntax1 <- paste0(
+    "model{\n",
+    "for(i in 1:N){\n",
+    "  y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n",
+    "}\n",
+    "}"
+  )
+
+  if("RoBMA" %in% rownames(installed.packages())){
+    require("RoBMA")
+  }else{
+    skip()
+  }
+
+  fit1 <- JAGS_fit(
+    model_syntax = model_syntax1, data = data, prior_list = prior_list1,
+    formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1)
+
+  mixed_posteriors <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters = names(attr(fit1, "prior_list"))
+  )
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-intercept", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors, "mu_intercept", prior = T, dots_prior = list(col = "grey"))
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_cont1", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors, "mu_x_cont1", prior = T, dots_prior = list(col = "grey"))
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac2t", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors, "mu_x_fac2t", prior = T, dots_prior = list(col = "grey"))
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac3t", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors, "mu_x_fac3t", prior = T, dots_prior = list(col = "grey"))
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-sigma", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors, "sigma", prior = T, dots_prior = list(col = "grey"))
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-bias-PET", function(){
+    PET <- mixed_posteriors$bias[,"PET",drop=FALSE]
+    attributes(PET) <- c(attributes(PET), attributes(mixed_posteriors$bias)[!names(attributes(mixed_posteriors$bias)) %in% c("dimnames", "dim")])
+    attr(PET, "prior_list")[!sapply(attr(PET, "prior_list"), is.prior.PET)] <- lapply(1:sum(!sapply(attr(PET, "prior_list"), is.prior.PET)), function(i) prior("point", list(0)))
+    attr(PET, "prior_list")[ sapply(attr(PET, "prior_list"), is.prior.PET)] <- lapply(attr(PET, "prior_list")[sapply(attr(PET, "prior_list"), is.prior.PET)], function(p) {
+      class(p) <- class(p)[!class(p) %in% "prior.PET"]
+      return(p)
+    })
+    plot_posterior(list(PET = PET), "PET", prior = T, dots_prior = list(col = "grey"))
+  })
+
+
+  mixed_posteriors_conditional1 <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "mu_intercept",
+    conditional = "mu_intercept",
+    force_plots = TRUE
+  )
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-intercept-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors_conditional1, "mu_intercept", prior = TRUE, dots_prior = list(col = "grey"))
+  })
+
+  mixed_posteriors_conditional2 <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "mu_x_cont1",
+    conditional = "mu_x_cont1",
+    force_plots = TRUE
+  )
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_cont1-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors_conditional2, "mu_x_cont1", prior = TRUE, dots_prior = list(col = "grey"))
+  })
+
+  mixed_posteriors_conditional3 <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "mu_x_fac2t",
+    conditional = "mu_x_fac2t",
+    force_plots = TRUE
+  )
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac2t-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors_conditional3, "mu_x_fac2t", prior = TRUE, dots_prior = list(col = "grey"))
+  })
+
+  mixed_posteriors_conditional4 <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "mu_x_fac3t",
+    conditional = "mu_x_fac3t",
+    force_plots = TRUE
+  )
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-x_fac3t-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors_conditional4, "mu_x_fac3t", prior = TRUE, dots_prior = list(col = "grey"))
+  })
+
+
+  mixed_posteriors_conditional5a <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "bias"
+  )
+
+  mixed_posteriors_conditional5b <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "bias",
+    conditional = "bias",
+    force_plots = TRUE
+  )
+
+  mixed_posteriors_conditional6a <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "bias",
+    conditional = "PET",
+    force_plots = TRUE
+  )
+
+  mixed_posteriors_conditional6b <- as_mixed_posteriors(
+    mode       = fit1,
+    parameters  = "bias",
+    conditional = "omega",
+    force_plots = TRUE
+  )
+
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-weightfunction", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors_conditional5a, parameter = "weightfunction", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2),  dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)))
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-weightfunction-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4))
+    plot_posterior(mixed_posteriors_conditional6b, parameter = "weightfunction", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2),  dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)))
+  })
+
+#   # TODO: at some point
+#   vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PETPEESE", function(){
+#     oldpar <- graphics::par(no.readonly = TRUE)
+#     on.exit(graphics::par(mar = oldpar[["mar"]]))
+#     par(mar = c(4, 4, 1, 4))
+#     plot_posterior(mixed_posteriors_conditional5a, parameter = "PETPEESE", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2),  dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)))
+#   })
+#
+#   vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PETPEESE-con", function(){
+#     oldpar <- graphics::par(no.readonly = TRUE)
+#     on.exit(graphics::par(mar = oldpar[["mar"]]))
+#     par(mar = c(4, 4, 1, 4))
+#     plot_posterior(mixed_posteriors_conditional6b, parameter = "PETPEESE", prior = TRUE, col = "black", col.fill = ggplot2::alpha("grey", 0.2),  dots_prior = list(col = "red", col.fill = ggplot2::alpha("red", 0.5)))
+#   })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-PET-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4), mfrow = c(3, 1))
+    hist(mixed_posteriors_conditional5a$bias[,"PET"], breaks = 50, col = "grey", main = "PET", xlab = "PET")
+    hist(mixed_posteriors_conditional5b$bias[,"PET"], breaks = 50, col = "grey", main = "PET", xlab = "PET")
+    hist(mixed_posteriors_conditional6a$bias[,"PET"], breaks = 50, col = "grey", main = "PET", xlab = "PET")
+  })
+
+  vdiffr::expect_doppelganger("model-averaging-plot-ss-posterior-omega-con", function(){
+    oldpar <- graphics::par(no.readonly = TRUE)
+    on.exit(graphics::par(mar = oldpar[["mar"]]))
+    par(mar = c(4, 4, 1, 4), mfrow = c(3, 3))
+    hist(mixed_posteriors_conditional5a$bias[,"omega[0,0.025]"], breaks = 50, col = "grey", main = "omega[0,0.025]", xlab = "omega[0,0.025]")
+    hist(mixed_posteriors_conditional5a$bias[,"omega[0.025,0.05]"], breaks = 50, col = "grey", main = "omega[0.025,0.05]", xlab = "omega[0.025,0.05]")
+    hist(mixed_posteriors_conditional5a$bias[,"omega[0.05,0.975]"], breaks = 50, col = "grey", main = "omega[0.05,0.975]", xlab = "omega[0.05,0.975]")
+
+    hist(mixed_posteriors_conditional5b$bias[,"omega[0,0.025]"], breaks = 50, col = "grey", main = "omega[0,0.025]", xlab = "omega[0,0.025]")
+    hist(mixed_posteriors_conditional5b$bias[,"omega[0.025,0.05]"], breaks = 50, col = "grey", main = "omega[0.025,0.05]", xlab = "omega[0.025,0.05]")
+    hist(mixed_posteriors_conditional5b$bias[,"omega[0.05,0.975]"], breaks = 50, col = "grey", main = "omega[0.05,0.975]", xlab = "omega[0.05,0.975]")
+
+    hist(mixed_posteriors_conditional6b$bias[,"omega[0,0.025]"], breaks = 50, col = "grey", main = "omega[0,0.025]", xlab = "omega[0,0.025]")
+    hist(mixed_posteriors_conditional6b$bias[,"omega[0.025,0.05]"], breaks = 50, col = "grey", main = "omega[0.025,0.05]", xlab = "omega[0.025,0.05]")
+    hist(mixed_posteriors_conditional6b$bias[,"omega[0.05,0.975]"], breaks = 50, col = "grey", main = "omega[0.05,0.975]", xlab = "omega[0.05,0.975]")
+  })
+
+
+
+
 })
diff --git a/tests/testthat/test-priors-print.R b/tests/testthat/test-priors-print.R
index e8dd139e..c5a9df63 100644
--- a/tests/testthat/test-priors-print.R
+++ b/tests/testthat/test-priors-print.R
@@ -124,10 +124,45 @@ test_that("Prior print function works", {
 
   p21 <- prior_spike_and_slab(prior("gamma", list(1, 2), list(0, Inf)),
                               prior_inclusion = prior("beta", list(3, 2)))
+  p22 <- prior_mixture(
+    list(
+      prior("normal", list(0,  1)),
+      prior("normal", list(-3, 1)),
+      prior("gamma",  list(5, 10))
+    )
+  )
+  p23 <- prior_mixture(
+    list(
+      prior("normal", list(0,  1), prior_weights = 1),
+      prior("normal", list(-3, 1), prior_weights = 5),
+      prior("gamma",  list(5, 10), prior_weights = 1)
+    ),
+    is_null = c(T, F, T)
+  )
+  p24 <- prior_mixture(
+    list(
+      prior("normal", list(0,  1), prior_weights = 1),
+      prior("normal", list(-3, 1), prior_weights = 5)
+    ),
+    components = c("b", "a")
+  )
+
   expect_equal(utils::capture.output(print(p21)), "Gamma(1, 2) * Beta(3, 2)")
   expect_equal(utils::capture.output(print(p21, short_name = TRUE)), "G(1, 2) * B(3, 2)")
+  expect_equal(utils::capture.output(print(p22, parameter_names = TRUE)), c(
+    "alternative:", "  (1/3) * Normal(mean = 0, sd = 1)", "  (1/3) * Normal(mean = -3, sd = 1)", "  (1/3) * Gamma(shape = 5, rate = 10)"
+  ))
+  expect_equal(utils::capture.output(print(p23, short_name = TRUE)), c(
+    "alternative:", "  (1/7) * N(0, 1)", "null:", "  (5/7) * N(-3, 1)", "  (1/7) * G(5, 10)"
+  ))
+  expect_equal(utils::capture.output(print(p24)), c(
+    "b:", "  (1/6) * Normal(0, 1)",  "a:", "  (5/6) * Normal(-3, 1)"
+  ))
   vdiffr::expect_doppelganger("priors-print-4", function(){
     empty_plot()
     text(0.5, 1, print(p21, plot = TRUE))
+    text(0.5, 0.9, print(p22, plot = TRUE))
+    text(0.5, 0.8, print(p23, plot = TRUE))
+    text(0.5, 0.7, print(p24, plot = TRUE))
   })
 })
diff --git a/tests/testthat/test-priors.R b/tests/testthat/test-priors.R
index e6fc96a4..1be5a549 100644
--- a/tests/testthat/test-priors.R
+++ b/tests/testthat/test-priors.R
@@ -22,12 +22,13 @@ test_prior          <- function(prior, skip_moments = FALSE){
   }
   # tests density function
   lines(prior, individual = TRUE)
+
   # tests quantile function
-  if(!is.prior.spike_and_slab(prior)){
+  if(!is.prior.spike_and_slab(prior) && !is.prior.mixture(prior)){
     abline(v = quant(prior, 0.5), col = "blue", lwd = 2)
   }
   # tests that pdf(q(x)) == x
-  if(!is.prior.point(prior) & !is.prior.discrete(prior) & !is.prior.spike_and_slab(prior)){
+  if(!is.prior.point(prior) && !is.prior.discrete(prior) && !is.prior.spike_and_slab(prior) && !is.prior.mixture(prior)){
     expect_equal(.25, cdf(prior, quant(prior, 0.25)), tolerance = 1e-4)
     expect_equal(.25, ccdf(prior, quant(prior, 0.75)), tolerance = 1e-4)
   }
@@ -328,3 +329,52 @@ test_that("Meandif prior distribution works", {
   vdiffr::expect_doppelganger("prior-meandif-2-5", function()test_meandif(p2.5))
 
 })
+
+test_that("Prior mixture distributions work", {
+
+  p1 <- prior_mixture(
+    list(
+      prior("normal", list(0,  1)),
+      prior("normal", list(-3, 1)),
+      prior("gamma",  list(5, 10))
+    )
+  )
+  p2 <- prior_mixture(
+    list(
+      prior("normal", list(0,  1), prior_weights = 1),
+      prior("normal", list(-3, 1), prior_weights = 5),
+      prior("gamma",  list(5, 10), prior_weights = 1)
+    ),
+    is_null = c(T, F, T)
+  )
+  p3 <- prior_mixture(
+    list(
+      prior("normal", list(0,  1), prior_weights = 1),
+      prior("normal", list(-3, 1), prior_weights = 5)
+    ),
+    components = c("b", "a")
+  )
+
+  vdiffr::expect_doppelganger("prior-mixture-1", function()test_prior(p1, skip_moments = TRUE))
+  vdiffr::expect_doppelganger("prior-mixture-2", function()test_prior(p2, skip_moments = TRUE))
+  vdiffr::expect_doppelganger("prior-mixture-3", function()test_prior(p3, skip_moments = TRUE))
+
+
+  # random number generator from complex mixture priors
+  p4 <- prior_mixture(list(
+    prior("spike", list(0), prior_weights = 1),
+    prior_factor("mnormal", list(0, 10), contrast = "orthonormal", prior_weights = 3),
+    prior_factor("mnormal", list(0, 1),  contrast = "orthonormal")
+    ),
+    is_null = c(T, F, F)
+  )
+
+  for(i in seq_along(p4)){
+    p4[[i]]$parameters[["K"]] <- 3
+  }
+
+
+  vdiffr::expect_doppelganger("prior-mixture-4", function()hist(rng(p4, 10000, transform_factor_samples = FALSE), main = print(p4, plot = T), breaks = 50, freq = FALSE))
+  vdiffr::expect_doppelganger("prior-mixture-5", function()hist(rng(p4, 10000, transform_factor_samples = TRUE), main = print(p4, plot = T), breaks = 50, freq = FALSE))
+
+})
diff --git a/tests/testthat/test-summary-tables.R b/tests/testthat/test-summary-tables.R
index f243a290..9c6657ca 100644
--- a/tests/testthat/test-summary-tables.R
+++ b/tests/testthat/test-summary-tables.R
@@ -56,7 +56,7 @@ test_that("Summary tables functions work",{
   runjags_summary <- models[[2]]$fit_summary
   expect_equal(colnames(runjags_summary), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"))
   expect_equal(rownames(runjags_summary), c("m", "omega[0,0.05]", "omega[0.05,1]"))
-  expect_equal(unname(unlist(runjags_summary[1,])), c(0.155080816, 0.197817354, -0.247495448, 0.167295089, 0.496803251, 0.009208408, 0.047000000, 461.000000000, NA), tolerance = 1e-4)
+  expect_equal(unname(unlist(runjags_summary[1,])), c(0.155080816, 0.197817354, -0.247495448, 0.167295089, 0.496803251, 0.009208408, 0.0466 , 461.4872, NA), tolerance = 1e-4)
 
   # ensemble estimates
   estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("m", "omega"), probs = c(.025, 0.95))
@@ -92,14 +92,14 @@ test_that("Summary tables functions work",{
   expect_equal(unname(as.vector(diagnostics_table[,3])), c("", "omega[one-sided: .05] ~ CumDirichlet(1, 1)", "omega[one-sided: .5, .05] ~ CumDirichlet(1, 1, 1)"))
   expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.01019039, 0.01348211, 0.01061287), tolerance = 1e-4)
   expect_equal(unname(as.vector(diagnostics_table[,5])), c(0.048, 0.047, 0.045), tolerance = 1e-3)
-  expect_equal(unname(as.vector(diagnostics_table[,6])), c(434, 461, 500))
+  expect_equal(unname(as.vector(diagnostics_table[,6])), c(434, 461, 500), tolerance = 1e-3)
   expect_equal(unname(as.vector(diagnostics_table[,7])), c(NA, NA, NA))
 
 
   ### test additional settings
   # transformations
   runjags_summary2t <- runjags_estimates_table(fit2, transformations = list("m" = list(fun = exp)))
-  expect_equal(exp(models[[3]]$fit_summary[1,c("lCI","Median","uCI","MCMC_error")]), runjags_summary2t[1,c("lCI","Median","uCI","MCMC_error")], tolerance = 1e-5)
+  expect_equal(exp(as.data.frame(models[[3]]$fit_summary[1,c("lCI","Median","uCI")])), as.data.frame(runjags_summary2t[1,c("lCI","Median","uCI")]), tolerance = 1e-5)
   expect_equal(colnames(models[[3]]$fit_summary), colnames(runjags_summary2t))
   expect_equal(rownames(models[[3]]$fit_summary), rownames(runjags_summary2t))
 
@@ -475,26 +475,26 @@ test_that("Summary tables functions work (formulas + factors)",{
   expect_equal(unname(as.vector(diagnostics_table[,4])), c("", "", "", "orthonormal contrast: mNormal(0, 1)"))
   expect_equal(unname(as.vector(diagnostics_table[,5])), c(0.003223670, 0.004142589, 0.001676136, 0.001959310), tolerance = 1e-3)
   expect_equal(unname(as.vector(diagnostics_table[,6])), c(0.013, 0.017, 0.011, 0.011), tolerance = 1e-3)
-  expect_equal(unname(as.vector(diagnostics_table[,7])), c(5559, 3526, 8660, 7969))
+  expect_equal(unname(as.vector(diagnostics_table[,7])), c(5559, 3526, 8660, 7969), tolerance = 1e-3)
   expect_equal(unname(as.vector(diagnostics_table[,8])), c(1.001154, 1.000955, 1.000125, 1.000658), tolerance = 1e-3)
 
 
   ### test additional settings
   # transformations of orthonormal contrast to differences from the mean
-  runjags_summary_t <- runjags_estimates_table(fit3, transform_factors = TRUE)
+  runjags_summary_t <- suppressMessages(runjags_estimates_table(fit3, transform_factors = TRUE))
   expect_equal(colnames(runjags_summary_t), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"))
   expect_equal(rownames(runjags_summary_t), c("(mu) intercept","(mu) x_cont1","(mu) x_fac3o [dif: A]","(mu) x_fac3o [dif: B]","(mu) x_fac3o [dif: C]", "(mu) x_cont1:x_fac3o [dif: A]", "(mu) x_cont1:x_fac3o [dif: B]", "(mu) x_cont1:x_fac3o [dif: C]", "sigma" ))
   expect_equal(capture_output_lines(runjags_summary_t, print = TRUE, width = 150),
                c("                                Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat",
                  "(mu) intercept                 0.188 0.121 -0.051  0.188 0.429     0.00099          0.008 14975 1.000",
                  "(mu) x_cont1                   0.324 0.140  0.047  0.324 0.597     0.00112          0.008 15680 1.000",
-                 "(mu) x_fac3o [dif: A]         -0.010 0.168 -0.337 -0.011 0.321     0.00132          0.008 15278 1.000",
-                 "(mu) x_fac3o [dif: B]         -0.064 0.170 -0.397 -0.064 0.270     0.00134          0.008 15081 1.000",
-                 "(mu) x_fac3o [dif: C]          0.074 0.167 -0.251  0.072 0.404     0.00132          0.008 15630 1.000",
-                 "(mu) x_cont1:x_fac3o [dif: A] -0.283 0.197 -0.668 -0.283 0.105     0.00156          0.008 15581 1.000",
-                 "(mu) x_cont1:x_fac3o [dif: B]  0.164 0.194 -0.221  0.164 0.539     0.00153          0.008 14954 1.000",
-                 "(mu) x_cont1:x_fac3o [dif: C]  0.119 0.202 -0.275  0.118 0.521     0.00160          0.008 15372 1.000",
-                 "sigma                          0.925 0.090  0.770  0.918 1.119     0.00100          0.011  7969 1.001"
+                 "(mu) x_fac3o [dif: A]         -0.010 0.168 -0.337 -0.011 0.321     0.00134          0.008 15720 1.000",
+                 "(mu) x_fac3o [dif: B]         -0.064 0.170 -0.397 -0.064 0.270     0.00139          0.008 14958 1.000",
+                 "(mu) x_fac3o [dif: C]          0.074 0.167 -0.251  0.072 0.404     0.00133          0.008 15737 1.000",
+                 "(mu) x_cont1:x_fac3o [dif: A] -0.283 0.197 -0.668 -0.283 0.105     0.00158          0.008 15659 1.000",
+                 "(mu) x_cont1:x_fac3o [dif: B]  0.164 0.194 -0.221  0.164 0.539     0.00160          0.008 14777 1.000",
+                 "(mu) x_cont1:x_fac3o [dif: C]  0.119 0.202 -0.275  0.118 0.521     0.00161          0.008 15778 1.000",
+                 "sigma                          0.925 0.090  0.770  0.918 1.119     0.00101          0.011  7969 1.001"
                ))
 
 
@@ -512,12 +512,12 @@ test_that("Summary tables functions work (formulas + factors)",{
                  "(mu) x_cont1:x_fac3o [dif: C]  0.005  0.000  0.000 0.000"
                ))
   # transform estimates
-  runjags_summary_t2 <- runjags_estimates_table(fit1, transform_factors = FALSE, transformations = list("mu_x_fac2t" = list(fun = exp)))
+  runjags_summary_t2 <- suppressMessages(runjags_estimates_table(fit1, transform_factors = FALSE, transformations = list("mu_x_fac2t" = list(fun = exp))))
   expect_equal(capture_output_lines(runjags_summary_t2, print = TRUE, width = 150),
                c("                 Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat",
                  "(mu) intercept  0.145 0.200 -0.245  0.144 0.541     0.00338          0.017  3526 1.001",
                  "(mu) x_cont1    0.327 0.139  0.052  0.327 0.602     0.00111          0.008 15725 1.000",
-                 "(mu) x_fac3t[B] 0.006 0.281 -0.550  0.011 0.550     0.00414          0.015  4596 1.001",
+                 "(mu) x_fac3t[B] 0.006 0.281 -0.550  0.011 0.550     0.00415          0.015  4596 1.001",
                  "(mu) x_fac3t[C] 0.118 0.277 -0.433  0.120 0.656     0.00407          0.015  4630 1.001",
                  "sigma           0.926 0.089  0.774  0.918 1.117     0.00099          0.011  8016 1.000"
                ))
@@ -541,7 +541,7 @@ test_that("Summary tables functions work (formulas + factors)",{
                  "(mu) x_fac3o[2]         -0.012 0.205 -0.412 -0.013 0.393     0.00164          0.008 15720 1.000",
                  "(mu) x_cont1:x_fac3o[1] -0.032 0.243 -0.507 -0.033 0.448     0.00196          0.008 15383 1.000",
                  "(mu) x_cont1:x_fac3o[2] -0.347 0.242 -0.818 -0.347 0.128     0.00193          0.008 15659 1.000",
-                 "sigma                    0.925 0.090  0.770  0.918 1.119     0.00100          0.011  7969 1.001"
+                 "sigma                    0.925 0.090  0.770  0.918 1.119     0.00101          0.011  7969 1.001"
 
                ))
   expect_equal(capture_output_lines(estimates_table,   print = TRUE, width = 150),
@@ -571,8 +571,8 @@ test_that("Summary tables functions work (formulas + factors)",{
                ))
   expect_equal(capture_output_lines(diagnostics_table, print = TRUE, width = 180),
                c(" Model Prior (mu) x_cont1          Prior (mu) x_fac3o              Prior (mu) x_cont1:x_fac3o     max[error(MCMC)] max[error(MCMC)/SD] min(ESS) max(R-hat)",
-                 "     1                                                                                                     0.00322               0.013     5559      1.001",
-                 "     2       Normal(0, 1)                                                                                  0.00414               0.017     3526      1.001",
+                 "     1                                                                                                     0.00323               0.013     5559      1.001",
+                 "     2       Normal(0, 1)                                                                                  0.00415               0.017     3526      1.001",
                  "     3                    orthonormal contrast: mNormal(0, 1)                                              0.00168               0.011     8660      1.000",
                  "     4       Normal(0, 1) orthonormal contrast: mNormal(0, 1) orthonormal contrast: mNormal(0, 1)          0.00196               0.011     7969      1.001"
                ))
@@ -710,7 +710,7 @@ test_that("Summary tables functions work (indepdent factors)",{
   expect_equal(unname(as.vector(diagnostics_table[,2])), c("independent contrast: Spike(0)","independent contrast: Normal(0, 0.25)"))
   expect_equal(unname(as.vector(diagnostics_table[,3])), c(0.0008277888, 0.0010673515), tolerance = 1e-3)
   expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.010, 0.011), tolerance = 1e-3)
-  expect_equal(unname(as.vector(diagnostics_table[,5])), c(9564, 8145))
+  expect_equal(unname(as.vector(diagnostics_table[,5])), c(9564, 8145), tolerance = 1e-3)
 
 })
 
@@ -813,7 +813,7 @@ test_that("Summary tables functions work (meandif factors)",{
   expect_equal(unname(unlist(runjags_summary[1,])), c(2.616574e-02,8.256672e-02,-1.369357e-01,2.621934e-02,1.851191e-01,6.471943e-04,8.000000e-03,1.627600e+04,9.999001e-01), tolerance = 1e-3)
 
   # ensemble estimates
-  estimates_table <- ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_fac3"), probs = c(.025, 0.95), transform_factors = TRUE)
+  estimates_table <- suppressMessages(ensemble_estimates_table(mixed_posteriors, parameters = c("mu_x_fac3"), probs = c(.025, 0.95), transform_factors = TRUE))
   expect_equal(colnames(estimates_table), c("Mean", "Median", "0.025",  "0.95"))
   expect_equal(rownames(estimates_table), c("(mu) x_fac3 [dif: A]", "(mu) x_fac3 [dif: B]", "(mu) x_fac3 [dif: C]"))
   expect_equal(unname(unlist(estimates_table[1,])), c(-0.2074503, -0.2206674, -0.4204564,  0.0000000), tolerance = 1e-3)
@@ -846,7 +846,7 @@ test_that("Summary tables functions work (meandif factors)",{
   expect_equal(unname(as.vector(diagnostics_table[,2])), c("", "mean difference contrast: mNormal(0, 0.2)"))
   expect_equal(unname(as.vector(diagnostics_table[,3])), c(0.0006707336, 0.0007978420), tolerance = 1e-3)
   expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.01, 0.01), tolerance = 1e-3)
-  expect_equal(unname(as.vector(diagnostics_table[,5])), c(9676, 9871))
+  expect_equal(unname(as.vector(diagnostics_table[,5])), c(9676, 9871), tolerance = 1e-3)
 
 })
 
@@ -901,23 +901,20 @@ test_that("Summary tables functions work (spike and slab priors)",{
     "}"
   )
 
-  log_posterior <- function(parameters, data){
-    sum(stats::dnorm(data$y, parameters[["mu"]], parameters[["sigma"]], log = TRUE))
-  }
-
   fit0 <- JAGS_fit(
     model_syntax = model_syntax, data = data, prior_list = prior_list,
     formula_list = formula_list0, formula_data_list = formula_data_list, formula_prior_list = formula_prior_list0, seed = 1)
 
+  # bridge sampling cannot be computer for spike and slab priors - using a dummy value for marglik
   marglik0 <- list(logml = 0)
   class(marglik0) <- "bridge"
+
   # mix posteriors
   models <- list(
     list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0), prior_weights = 1)
   )
   models <- models_inference(models)
 
-  model_summary_table(models[[1]])
   ### checking summary functions
   # model summary
   model_summary <- model_summary_table(models[[1]])
@@ -925,26 +922,52 @@ test_that("Summary tables functions work (spike and slab priors)",{
   expect_equal(model_summary[,4], c("Parameter prior distributions", "(mu) intercept ~ Normal(0, 5)", "(mu) x_cont1 ~ Normal(0, 0.5) * Beta(1, 1)", "(mu) x_fac2t ~ treatment contrast: Normal(0, 1) * Beta(1, 1)", "(mu) x_fac3o ~ orthonormal contrast: mNormal(0, 1) * Spike(0.5)", "sigma ~ Lognormal(0, 1)"))
 
   model_estimates <- runjags_estimates_table(fit0)
-  expect_equal(colnames(model_estimates), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"))
-  expect_equal(rownames(model_estimates), c("(mu) intercept", "(mu) x_cont1", "(mu) x_cont1 (inclusion)", "(mu) x_fac2t[B]", "(mu) x_fac2t (inclusion)", "(mu) x_fac3o[1]", "(mu) x_fac3o[2]", "(mu) x_fac3o (inclusion)", "sigma"))
-  expect_equal(unname(unlist(model_estimates[7,])), c(-1.776437e-03, 4.269207e-02, 0.000000e+00, 0.000000e+00, 0.000000e+00, 3.428435e-04, 8.000000e-03, 1.550600e+04, 1.001065e+00), tolerance = 1e-3)
-
-  model_estimates <- runjags_estimates_table(fit0, transform_factors = TRUE, conditional = TRUE)
-  expect_equal(colnames(model_estimates), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"))
-  expect_equal(rownames(model_estimates), c("(mu) intercept", "(mu) x_cont1", "(mu) x_cont1 (inclusion)", "(mu) x_fac2t[B]", "(mu) x_fac2t (inclusion)", "(mu) x_fac3o [dif: A]", "(mu) x_fac3o [dif: B]", "(mu) x_fac3o [dif: C]", "(mu) x_fac3o (inclusion)", "sigma"))
-  expect_equal(unname(unlist(model_estimates[8,])), c(0.0626582174, 0.1661778973, -0.2621073424, 0.0591205499, 0.3954805352,  NA, NA, NA, NA), tolerance = 1e-3)
-
-  model_estimates <- runjags_estimates_table(fit0, transform_factors = TRUE, conditional = TRUE, remove_inclusion = TRUE)
-  expect_equal(colnames(model_estimates), c("Mean", "SD", "lCI", "Median", "uCI", "MCMC_error", "MCMC_SD_error", "ESS", "R_hat"))
-  expect_equal(rownames(model_estimates), c("(mu) intercept", "(mu) x_cont1", "(mu) x_fac2t[B]", "(mu) x_fac3o [dif: A]", "(mu) x_fac3o [dif: B]", "(mu) x_fac3o [dif: C]", "sigma"))
-  expect_equal(unname(unlist(model_estimates[2,])), c(3.040927e-01, 1.355633e-01, 3.256895e-02, 3.058346e-01, 5.678668e-01, 2.298187e-03, 1.300000e-02, 5.720000e+03, 1.000421e+00), tolerance = 1e-3)
+  testthat::expect_equal(capture_output_lines(print(model_estimates), width = 150), c(
+                         "                           Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat",
+                         "(mu) intercept            0.194 0.132 -0.072  0.195 0.456     0.00132          0.010 10084 1.000",
+                         "(mu) x_cont1 (inclusion)  0.780    NA     NA     NA    NA          NA             NA    NA    NA",
+                         "(mu) x_cont1              0.237 0.174  0.000  0.256 0.555     0.00230          0.013  5720 1.000",
+                         "(mu) x_fac2t (inclusion)  0.186    NA     NA     NA    NA          NA             NA    NA    NA",
+                         "(mu) x_fac2t[B]           0.006 0.105 -0.233  0.000 0.299     0.00123          0.012  7310 1.002",
+                         "(mu) x_fac3o (inclusion)  0.040    NA     NA     NA    NA          NA             NA    NA    NA",
+                         "(mu) x_fac3o[1]           0.003 0.043  0.000  0.000 0.003     0.00034          0.008 15764 1.001",
+                         "(mu) x_fac3o[2]          -0.002 0.043  0.000  0.000 0.000     0.00035          0.008 15506 1.001",
+                         "sigma                     0.922 0.088  0.772  0.915 1.113     0.00095          0.011  8458 1.000"
+  ))
+
+  model_estimates <- suppressMessages(runjags_estimates_table(fit0, transform_factors = TRUE, conditional = TRUE))
+  testthat::expect_equal(capture_output_lines(print(model_estimates), width = 150), c(
+    "                           Mean    SD    lCI Median   uCI",
+    "(mu) intercept            0.194 0.132 -0.072  0.195 0.456",
+    "(mu) x_cont1 (inclusion)  0.780    NA     NA     NA    NA",
+    "(mu) x_cont1              0.304 0.136  0.033  0.306 0.568",
+    "(mu) x_fac2t (inclusion)  0.186    NA     NA     NA    NA",
+    "(mu) x_fac2t[B]           0.033 0.241 -0.435  0.031 0.507",
+    "(mu) x_fac3o (inclusion)  0.040    NA     NA     NA    NA",
+    "(mu) x_fac3o [dif: A]    -0.036 0.171 -0.359 -0.043 0.316",
+    "(mu) x_fac3o [dif: B]    -0.026 0.173 -0.367 -0.026 0.296",
+    "(mu) x_fac3o [dif: C]     0.063 0.166 -0.262  0.059 0.395",
+    "sigma                     0.922 0.088  0.772  0.915 1.113"
+  ))
+
+  model_estimates <- suppressMessages(runjags_estimates_table(fit0, transform_factors = TRUE, conditional = TRUE, remove_inclusion = TRUE))
+  testthat::expect_equal(capture_output_lines(print(model_estimates), width = 150), c(
+    "                        Mean    SD    lCI Median   uCI",
+    "(mu) intercept         0.194 0.132 -0.072  0.195 0.456",
+    "(mu) x_cont1           0.304 0.136  0.033  0.306 0.568",
+    "(mu) x_fac2t[B]        0.033 0.241 -0.435  0.031 0.507",
+    "(mu) x_fac3o [dif: A] -0.036 0.171 -0.359 -0.043 0.316",
+    "(mu) x_fac3o [dif: B] -0.026 0.173 -0.367 -0.026 0.296",
+    "(mu) x_fac3o [dif: C]  0.063 0.166 -0.262  0.059 0.395",
+    "sigma                  0.922 0.088  0.772  0.915 1.113"
+  ))
 
   model_inference <- runjags_inference_table(fit0)
-  expect_equal(colnames(model_inference), c("Parameter", "prior_prob", "post_prob", "inclusion_BF"))
-  expect_equal(model_inference[,1], c("(mu) x_cont1", "(mu) x_fac2t", "(mu) x_fac3o"))
-  expect_equal(model_inference[,2], c(0.5, 0.5, 0.5))
-  expect_equal(model_inference[,3], c(0.7798125, 0.1864375, 0.0399375), tolerance = 1e-3)
-  expect_equal(model_inference[,4], c(3.54158388, 0.22916187, 0.04159885), tolerance = 1e-3)
+  expect_equal(colnames(model_inference), c("prior_prob", "post_prob", "inclusion_BF"))
+  expect_equal(rownames(model_inference), c("(mu) x_cont1", "(mu) x_fac2t", "(mu) x_fac3o"))
+  expect_equal(model_inference[,1], c(0.5, 0.5, 0.5))
+  expect_equal(model_inference[,2], c(0.7798125, 0.1864375, 0.0399375), tolerance = 1e-3)
+  expect_equal(model_inference[,3], c(3.54158388, 0.22916187, 0.04159885), tolerance = 1e-3)
 
   runjags_inference_empty <- runjags_inference_empty_table()
   expect_equivalent(nrow(runjags_inference_empty), 0)
@@ -1051,8 +1074,8 @@ test_that("Summary tables functions work (spike factors)",{
 
   # mix posteriors
   models <- list(
-    list(fit = fit0, marglik = marglik0, fit_summary = runjags_estimates_table(fit0, remove_spike_0 = FALSE, transform_factors = TRUE), prior_weights = 1),
-    list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1, remove_spike_0 = FALSE, transform_factors = TRUE), prior_weights = 1)
+    list(fit = fit0, marglik = marglik0, fit_summary = suppressMessages(runjags_estimates_table(fit0, remove_spike_0 = FALSE, transform_factors = TRUE)), prior_weights = 1),
+    list(fit = fit1, marglik = marglik1, fit_summary = suppressMessages(runjags_estimates_table(fit1, remove_spike_0 = FALSE, transform_factors = TRUE)), prior_weights = 1)
   )
   models <- models_inference(models)
 
@@ -1119,6 +1142,322 @@ test_that("Summary tables functions work (spike factors)",{
   expect_equal(unname(as.vector(diagnostics_table[,2])), c("mean difference contrast: mSpike(0)", "mean difference contrast: mNormal(0, 0.25)"))
   expect_equal(unname(as.vector(diagnostics_table[,3])), c(0.0004365069, 0.0006020573), tolerance = 1e-3)
   expect_equal(unname(as.vector(diagnostics_table[,4])), c(0.01, 0.01), tolerance = 1e-3)
-  expect_equal(unname(as.vector(diagnostics_table[,5])), c(9774, 10554))
+  expect_equal(unname(as.vector(diagnostics_table[,5])), c(0, 10554), tolerance = 1e-3)
+
+})
+
+test_that("Summary tables functions work (mixture priors)",{
+
+  skip_on_os(c("mac", "linux", "solaris")) # multivariate sampling does not exactly match across OSes
+  skip_on_cran()
+
+  set.seed(1)
+
+  data_formula <- data.frame(
+    x_cont1 = rnorm(300),
+    x_fac2t = factor(rep(c("A", "B"), 150), levels = c("A", "B")),
+    x_fac3t = factor(rep(c("A", "B", "C"), 100), levels = c("A", "B", "C"))
+  )
+  data <- list(
+    y = rnorm(300, -0.15 + 0.20 * data_formula$x_cont1 + ifelse(data_formula$x_fac3t == "A", 0.0, ifelse(data_formula$x_fac3t == "B", -0.2, 0.2)), ifelse(data_formula$x_fac2t == "A", 0.5, 1)),
+    N = 300
+  )
+
+  # create model with mix of a formula and free parameters ---
+  formula_list1 <- list(
+    mu    = ~ x_cont1 + x_fac3t
+  )
+  formula_data_list1 <- list(
+    mu    = data_formula
+  )
+  formula_prior_list1 <- list(
+    mu    = list(
+      "intercept"  = prior_mixture(
+        list(
+          prior("spike",   list(0),       prior_weights = 2),
+          prior("normal",  list(-1, 0.5), prior_weights = 1),
+          prior("normal",  list( 1, 0.5), prior_weights = 1)
+        ),
+        is_null = c(T, F, F)
+      ),
+      "x_cont1"    = prior_mixture(
+        list(
+          prior("spike",   list(0),    prior_weights = 1),
+          prior("normal",  list(0, 1), prior_weights = 1)
+        ),
+        is_null = c(T, F)
+      ),
+      "x_fac3t"    = prior_spike_and_slab(prior_factor("mnormal", list(0, 1), contrast = "orthonormal"),
+                                          prior_inclusion = prior("spike", list(0.5)))
+    )
+  )
+  attr(formula_prior_list1$mu$x_cont1, "multiply_by") <- "sigma"
+  prior_list1 <- list(
+    "sigma" = prior_mixture(
+      list(
+        prior("normal",    list(0, 1), truncation = list(0, Inf)),
+        prior("lognormal", list(0, 1))
+      ),
+      components = c("normal", "lognormal")
+    ),
+    "bias"  = prior_mixture(list(
+      prior_none(prior_weights = 1),
+      prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/3),
+      prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/3),
+      prior_PET("normal", list(0, 1), prior_weights = 1/3)
+    ), is_null = c(T, F, F, F))
+  )
+  model_syntax1 <- paste0(
+    "model{\n",
+    "for(i in 1:N){\n",
+    "  y[i] ~ dnorm(mu[i], 1/pow(sigma, 2))\n",
+    "}\n",
+    "}"
+  )
+
+  if("RoBMA" %in% rownames(installed.packages())){
+    require("RoBMA")
+  }else{
+    skip()
+  }
+
+  fit1 <- JAGS_fit(
+    model_syntax = model_syntax1, data = data, prior_list = prior_list1,
+    formula_list = formula_list1, formula_data_list = formula_data_list1, formula_prior_list = formula_prior_list1)
+
+  # bridge sampling cannot be computer for spike and slab priors - using a dummy value for marglik
+  marglik1 <- list(logml = 0)
+  class(marglik1) <- "bridge"
+
+  # mix posteriors
+  models <- list(
+    list(fit = fit1, marglik = marglik1, fit_summary = runjags_estimates_table(fit1), prior_weights = 1)
+  )
+  models <- models_inference(models)
+
+  ### checking summary functions
+  # model summary
+  model_summary <- model_summary_table(models[[1]], short_name = TRUE)
+  expect_equal(model_summary[,1], c("Model  ", "Prior prob.  ", "log(marglik)  ", "Post. prob.  ", "Inclusion BF  ", "  "))
+  expect_equal(model_summary[,4], c(
+    "Parameter prior distributions",
+    "(mu) intercept ~ (2/4) * S(0) + (1/4) * N(-1, 0.5) + (1/4) * N(1, 0.5)",
+    "(mu) x_cont1 ~ (1/2) * S(0) + (1/2) * N(0, 1)",
+    "(mu) x_fac3t ~ orthonormal contrast: mN(0, 1) * S(0.5)",
+    "sigma ~ (1/2) * N(0, 1)[0, Inf] + (1/2) * Ln(0, 1)",
+    "bias ~ (1/2) * None + (0.33/2) * omega[2s: .05] ~ CumD(1, 1) + (0.33/2) * omega[1s: .05, .025] ~ CumD(1, 1, 1) + (0.33/2) * PET ~ N(0, 1)[0, Inf]"
+  ))
+
+  model_estimates <- runjags_estimates_table(fit1)
+  expect_equal(capture_output_lines(print(model_estimates), width = 150),  c(
+    "                               Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat",
+    "(mu) intercept (inclusion)    0.596    NA     NA     NA    NA          NA             NA    NA    NA",
+    "(mu) intercept               -0.087 0.080 -0.226 -0.098 0.000     0.00246          0.031  1067 1.002",
+    "(mu) x_cont1 (inclusion)      0.999    NA     NA     NA    NA          NA             NA    NA    NA",
+    "(mu) x_cont1                  0.279 0.063  0.154  0.280 0.401     0.00063          0.010 11015 1.000",
+    "(mu) x_fac3t (inclusion)      0.855    NA     NA     NA    NA          NA             NA    NA    NA",
+    "(mu) x_fac3t[1]               0.252 0.128  0.000  0.277 0.448     0.00467          0.037   939 1.016",
+    "(mu) x_fac3t[2]              -0.012 0.074 -0.167  0.000 0.137     0.00057          0.008 17039 1.001",
+    "sigma (inclusion: normal)     0.510    NA     NA     NA    NA          NA             NA    NA    NA",
+    "sigma (inclusion: lognormal)  0.490    NA     NA     NA    NA          NA             NA    NA    NA",
+    "sigma                         0.803 0.034  0.740  0.802 0.874     0.00039          0.011  7753 1.000",
+    "bias (inclusion)              0.497    NA     NA     NA    NA          NA             NA    NA    NA",
+    "PET                           0.130 0.377  0.000  0.000 1.410     0.00292          0.008 16826 1.000",
+    "omega[0,0.025]                1.000 0.000  1.000  1.000 1.000          NA             NA    NA    NA",
+    "omega[0.025,0.05]             0.865 0.248  0.139  1.000 1.000     0.00196          0.008 16000 1.000",
+    "omega[0.05,0.975]             0.809 0.316  0.053  1.000 1.000     0.00247          0.008 16361 1.000",
+    "omega[0.975,1]                0.889 0.267  0.076  1.000 1.000     0.00211          0.008 16128 1.000"
+  ))
+
+  model_estimates <- suppressMessages(runjags_estimates_table(fit1, transform_factors = TRUE, conditional = TRUE))
+  expect_equal(capture_output_lines(print(model_estimates), width = 150),  c(
+    "                               Mean    SD    lCI Median    uCI",
+    "(mu) intercept (inclusion)    0.596    NA     NA     NA     NA",
+    "(mu) intercept               -0.145 0.047 -0.238 -0.146 -0.052",
+    "(mu) x_cont1 (inclusion)      0.999    NA     NA     NA     NA",
+    "(mu) x_cont1                  0.279 0.062  0.155  0.280  0.401",
+    "(mu) x_fac3t (inclusion)      0.855    NA     NA     NA     NA",
+    "(mu) x_fac3t [dif: A]        -0.012 0.066 -0.141 -0.012  0.116",
+    "(mu) x_fac3t [dif: B]        -0.203 0.066 -0.333 -0.202 -0.074",
+    "(mu) x_fac3t [dif: C]         0.214 0.065  0.088  0.214  0.341",
+    "sigma (inclusion: normal)     0.510    NA     NA     NA     NA",
+    "sigma (inclusion: lognormal)  0.490    NA     NA     NA     NA",
+    "sigma[normal]                 0.804 0.034  0.740  0.802  0.872",
+    "sigma[lognormal]              0.803 0.034  0.740  0.802  0.875",
+    "bias (inclusion)              0.497    NA     NA     NA     NA",
+    "PET                           0.780 0.589  0.031  0.656  2.113",
+    "omega[0,0.025]                1.000 0.000  1.000  1.000  1.000",
+    "omega[0.025,0.05]             0.592 0.275  0.048  0.627  0.984",
+    "omega[0.05,0.975]             0.421 0.279  0.017  0.386  0.953",
+    "omega[0.975,1]                0.663 0.374  0.027  0.916  1.000"
+  ))
+
+  model_estimates <- runjags_estimates_table(fit1, transform_factors = TRUE, conditional = TRUE, remove_inclusion = TRUE)
+  expect_equal(capture_output_lines(print(model_estimates), width = 150),  c(
+    "                        Mean    SD    lCI Median    uCI",
+    "(mu) intercept        -0.145 0.047 -0.238 -0.146 -0.052",
+    "(mu) x_cont1           0.279 0.062  0.155  0.280  0.401",
+    "(mu) x_fac3t [dif: A] -0.012 0.066 -0.141 -0.012  0.116",
+    "(mu) x_fac3t [dif: B] -0.203 0.066 -0.333 -0.202 -0.074",
+    "(mu) x_fac3t [dif: C]  0.214 0.065  0.088  0.214  0.341",
+    "sigma[normal]          0.804 0.034  0.740  0.802  0.872",
+    "sigma[lognormal]       0.803 0.034  0.740  0.802  0.875",
+    "PET                    0.780 0.589  0.031  0.656  2.113",
+    "omega[0,0.025]         1.000 0.000  1.000  1.000  1.000",
+    "omega[0.025,0.05]      0.592 0.275  0.048  0.627  0.984",
+    "omega[0.05,0.975]      0.421 0.279  0.017  0.386  0.953",
+    "omega[0.975,1]         0.663 0.374  0.027  0.916  1.000"
+  ))
+
+  model_estimates <- runjags_estimates_table(fit1, transformations = list(
+    "mu_intercept" = list(fun = exp),
+    "mu_x_cont1"   = list(fun = exp),
+    "sigma"        = list(fun = exp),
+    "PET"          = list(fun = exp)
+  ))
+  expect_equal(capture_output_lines(print(model_estimates), width = 150),  c(
+    "                               Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat",
+    "(mu) intercept (inclusion)    0.596    NA     NA     NA    NA          NA             NA    NA    NA",
+    "(mu) intercept                0.920 0.073  0.798  0.907 1.000     0.00225          0.031  1061 1.002",
+    "(mu) x_cont1 (inclusion)      0.999    NA     NA     NA    NA          NA             NA    NA    NA",
+    "(mu) x_cont1                  1.324 0.083  1.166  1.323 1.494     0.00081          0.010 11242 1.000",
+    "(mu) x_fac3t (inclusion)      0.855    NA     NA     NA    NA          NA             NA    NA    NA",
+    "(mu) x_fac3t[1]               0.252 0.128  0.000  0.277 0.448     0.00467          0.037   939 1.016",
+    "(mu) x_fac3t[2]              -0.012 0.074 -0.167  0.000 0.137     0.00057          0.008 17039 1.001",
+    "sigma (inclusion: normal)     0.510    NA     NA     NA    NA          NA             NA    NA    NA",
+    "sigma (inclusion: lognormal)  0.490    NA     NA     NA    NA          NA             NA    NA    NA",
+    "sigma                         2.235 0.077  2.097  2.231 2.395     0.00088          0.011  7722 1.000",
+    "bias (inclusion)              0.497    NA     NA     NA    NA          NA             NA    NA    NA",
+    "PET                           1.288 1.525  1.000  1.000 4.095     0.01205          0.008 16030 1.093",
+    "omega[0,0.025]                1.000 0.000  1.000  1.000 1.000          NA             NA    NA    NA",
+    "omega[0.025,0.05]             0.865 0.248  0.139  1.000 1.000     0.00196          0.008 16000 1.000",
+    "omega[0.05,0.975]             0.809 0.316  0.053  1.000 1.000     0.00247          0.008 16361 1.000",
+    "omega[0.975,1]                0.889 0.267  0.076  1.000 1.000     0.00211          0.008 16128 1.000"
+  ))
+
+  model_estimates <- runjags_estimates_table(fit1, conditional = TRUE, remove_inclusion = TRUE, transformations = list(
+    "mu_intercept" = list(fun = exp),
+    "mu_x_cont1"   = list(fun = exp),
+    "sigma"        = list(fun = exp),
+    "PET"          = list(fun = exp)
+  ))
+  expect_equal(capture_output_lines(print(model_estimates), width = 150),  c(
+    "                    Mean    SD    lCI Median   uCI",
+    "(mu) intercept     0.866 0.041  0.788  0.864 0.949",
+    "(mu) x_cont1       1.325 0.083  1.168  1.323 1.494",
+    "(mu) x_fac3t[1]    0.295 0.081  0.138  0.295 0.454",
+    "(mu) x_fac3t[2]   -0.014 0.080 -0.173 -0.015 0.142",
+    "sigma[normal]      2.235 0.077  2.097  2.231 2.392",
+    "sigma[lognormal]   2.234 0.077  2.097  2.231 2.400",
+    "PET                2.726 3.384  1.032  1.927 8.272",
+    "omega[0,0.025]     1.000 0.000  1.000  1.000 1.000",
+    "omega[0.025,0.05]  0.592 0.275  0.048  0.627 0.984",
+    "omega[0.05,0.975]  0.421 0.279  0.017  0.386 0.953",
+    "omega[0.975,1]     0.663 0.374  0.027  0.916 1.000"
+  ))
+
+  model_inference <- runjags_inference_table(fit1)
+  expect_equal(capture_output_lines(print(model_inference), width = 150),  c(
+    "                  Prior prob. Post. prob. Inclusion BF",
+    "(mu) intercept          0.500       0.596        1.478",
+    "(mu) x_cont1            0.500       0.999      841.105",
+    "(mu) x_fac3t            0.500       0.855        5.894",
+    "sigma [normal]          0.500       0.510        1.041",
+    "sigma [lognormal]       0.500       0.490        0.961",
+    "bias                    0.500       0.497        0.989"
+  ))
+
+  model_inference <- update(model_inference, title = "Table 1", footnotes = c("Footnote 1", "Footnote 2"), logBF = TRUE)
+  expect_equal(capture_output_lines(print(model_inference), width = 150),  c(
+    "Table 1"                                                     ,
+    "                  Prior prob. Post. prob. log(Inclusion BF)",
+    "(mu) intercept          0.500       0.596             0.391",
+    "(mu) x_cont1            0.500       0.999             6.735",
+    "(mu) x_fac3t            0.500       0.855             1.774",
+    "sigma [normal]          0.500       0.510             0.040",
+    "sigma [lognormal]       0.500       0.490            -0.040",
+    "bias                    0.500       0.497            -0.011",
+    "Footnote 1"                                                 ,
+    "Footnote 2"  ))
+})
+
+test_that("Summary tables odd cases",{
+
+  set.seed(1)
+
+  data <- list(
+    y = rnorm(10),
+    N = 10
+  )
+
+  prior_list <- list(
+    "mu" = prior_mixture(
+      list(prior("spike", list(0))),
+      is_null = c(FALSE)
+    ),
+    "sigma" = prior_mixture(
+      list(prior("spike", list(1))),
+      is_null = c(TRUE)
+    ),
+    "beta" = prior("normal", list(0, 1))
+  )
+  model_syntax <- paste0(
+    "model{\n",
+    "for(i in 1:N){\n",
+    "  y[i] ~ dnorm(mu, 1/pow(sigma, 2))\n",
+    "}\n",
+    "}"
+  )
+
+  fit <- JAGS_fit(
+    model_syntax = model_syntax, data = data, prior_list = prior_list
+  )
+
+  expect_equal(capture_output_lines(print(runjags_estimates_table(fit)), width = 150),  c(
+    "                    Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat",
+    "mu (inclusion)     1.000    NA     NA     NA    NA          NA             NA    NA    NA",
+    "mu                 0.000 0.000  0.000  0.000 0.000     0.00000             NA     0    NA",
+    "sigma (inclusion)  0.000    NA     NA     NA    NA          NA             NA    NA    NA",
+    "sigma              1.000 0.000  1.000  1.000 1.000     0.00000             NA     0    NA",
+    "beta              -0.014 0.999 -1.985 -0.003 1.930     0.00805          0.008 15412 1.000"
+  ))
+
+  expect_equal(capture_output_lines(print(runjags_estimates_table(fit, conditional = TRUE)), width = 150),  c(
+    "                    Mean    SD    lCI Median   uCI",
+    "mu (inclusion)     1.000    NA     NA     NA    NA",
+    "mu                 0.000 0.000  0.000  0.000 0.000",
+    "sigma (inclusion)  0.000    NA     NA     NA    NA",
+    "sigma                NaN    NA     NA     NA    NA",
+    "beta              -0.014 0.999 -1.985 -0.003 1.930",
+    "\033[0;31mConditional summary for sigma parameter could not be computed due to no posterior samples.\033[0m"
+  ))
+
+  expect_equal(capture_output_lines(print(runjags_inference_table(fit)), width = 150),  c(
+    "      Prior prob. Post. prob. Inclusion BF",
+    "mu          1.000       1.000          Inf",
+    "sigma       0.000       0.000        0.000"
+  ))
+
+})
+
+test_that("Simplified interpret2 function", {
+
+  set.seed(1)
+  information <- list(
+    list(
+      inference_name        = "Effect",
+      inference_BF_name     = "BF10",
+      inference_BF          = 3.5,
+      estimate_name         = "mu",
+      estimate_samples      = rnorm(1000, 0.3, 0.15),
+      estimate_units        = "kg",
+      estimate_conditional  = FALSE
+    )
+  )
+
+  expect_equal(
+    interpret2(information, "RoBMA"),
+    "RoBMA found moderate evidence in favor of the Effect, BF10 = 3.50, with mean model-averaged estimate mu = 0.298 kg, 95% CI [-0.020,  0.601]."
+  )
 
 })