update actions workflows

.github/workflows/R-CMD-check.yaml

@@ -35,7 +35,7 @@ jobs:
       RSPM: ${{ matrix.config.rspm }}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
         with:

.github/workflows/build-website.yml

@@ -10,7 +10,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 

.github/workflows/testthat.yaml

@@ -14,7 +14,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
 

.github/workflows/vignette_checker.yml

@@ -32,7 +32,7 @@ jobs:
       GITHUB_PAT: ${{secrets.GITHUB_TOKEN}}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
         with:

docs/pkgdown.yml

@@ -1,5 +1,5 @@
-pandoc: 2.19.2
-pkgdown: 2.0.7
+pandoc: 2.17.1.1
+pkgdown: 2.0.3
 pkgdown_sha: ~
 articles:
   age_x_size: age_x_size.html
@@ -9,7 +9,7 @@ articles:
   index-notation: index-notation.html
   ipmr-introduction: ipmr-introduction.html
   proto-ipms: proto-ipms.html
-last_built: 2023-02-16T14:04Z
+last_built: 2024-06-11T21:34Z
 urls:
   reference: https://levisc8.github.io/ipmr/reference
   article: https://levisc8.github.io/ipmr/articles

paper/SI/R/00_case_study_1.Rmd

@@ -4,6 +4,8 @@ output: pdf_document
 urlcolor: blue
 ---
 
+\pagenumbering{gobble}
+
 ## Two versions of a simple model
 
 The first case study in this manuscript creates a model for _Carpobrotus spp_. The dataset used in this case study was collected in Havatselet Ha'Sharon, a suburb of Tel Aviv, Israel. The data were collected by drones taking aerial imagery of the population in successive years. Images were combined into a single high-resolution orthomosaic and georeferenced so the map from year 2 laid on top of the map from year 1. Flowers on each plant were counted using a point layer, and polygons were drawn around each ramet to estimate sizes and survival from year to year. Plants that had 0 flowers were classified as non-reproductive, and any plant with 1 or more flowers was classified as reproductive. This led to four regression models - survival, growth conditional on survival, probability of flowering, and number of flowers produced conditional on flowering. Finally, plants present in year 2 that were not present in year 1 were considered new recruits. The mean and variance of their sizes were computed, and this was used to model the recruit size distribution. 

paper/SI/R/01_case_study_2.Rmd

@@ -4,6 +4,8 @@ output: pdf_document
 urlcolor: blue
 ---
 
+\pagenumbering{gobble}
+
 ## A more complicated, age and size structured model
 
 Many life cycles cannot be described by a single, continuous state variable. For example, some plants are best modeled using height or diameter at breast height (DBH) as a state variable, and may also form a seed bank. Seeds in the seed bank can't have a value for height or DBH, but may lose their viability as they age. Thus, we require a discrete state to capture the dynamics of the seed bank. Models that include discrete states and/or multiple continuous state variables are _general IPMs_.