From 914bc364aed19db4a17e856f9cda33cf883a3307 Mon Sep 17 00:00:00 2001 From: Pius Korner Date: Mon, 9 Dec 2024 20:30:17 +0100 Subject: [PATCH] updates to stress that e-book stands alone (without printed version) --- index.Rmd | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/index.Rmd b/index.Rmd index e911cd3..77884af 100755 --- a/index.Rmd +++ b/index.Rmd @@ -1,6 +1,6 @@ --- title: "Bayesian Data Analysis in Ecology with R and Stan" -author: "Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jerôme Guélat, Bettina Almasi, Pius Korner-Nievergelt" +author: "Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jerôme Guélat, Bettina Almasi, Louis Hunninck, Pius Korner-Nievergelt" date: "`r Sys.Date()`" site: bookdown::bookdown_site documentclass: book @@ -18,19 +18,19 @@ knitr::include_graphics('images/cover.jpg', dpi = 150) ``` ## Why this book? {-} -In 2015, we wrote a statistics book for Master/PhD level Bayesian data analyses in ecology [@KornerNievergelt2015]. You can order it [here](https://www.elsevier.com/books/bayesian-data-analysis-in-ecology-using-linear-models-with-r-bugs-and-stan/korner-nievergelt/978-0-12-801370-0). People seemed to like it (e.g. [@Harju2016]). Since then, two parallel processes happen. First, we learn more and we become more confident in what we do, or what we do not, and why we do what we do. Second, several really clever people develop software that broaden the spectrum of ecological models that now easily can be applied by ecologists used to work with R. With this e-book, we open the possibility to add new or substantially revised material. In most of the time, it should be in a state that it can be printed and used together with the book as handout for our stats courses. +In 2015, we wrote a statistics book on Master/PhD-level Bayesian data analyses in ecology [@KornerNievergelt2015] based on scripts we used for our statistics courses. You can order it [here](https://www.elsevier.com/books/bayesian-data-analysis-in-ecology-using-linear-models-with-r-bugs-and-stan/korner-nievergelt/978-0-12-801370-0). People seemed to like it (e.g. [@Harju2016]). Since then, two parallel processes happen. First, we learned more and we became more confident in what we do and what we don't do, and why we do what we do. Second, clever people developed software that broaden the spectrum of ecological models that now easily can be applied by ecologists used to work with R. With this e-book, we add new material and substantially revised text of the printed book. ## About this book {-} -We do not copy text from the book into the e-book. Therefore, we refer to the book [@KornerNievergelt2015] for reading about the basic theory on doing Bayesian data analyses using linear models. However, Chapters 1 to 17 of this dynamic e-book correspond to the book chapters. In each chapter, we may provide updated R-codes and/or additional material. The following chapters contain completely new material that we think may be useful for ecologists. +We understand this e-book as a dynamic project. Based on contributions from readers and based on further developments in R and its packages, we plan to continuously update the text. On the other hand, at any point in time, the published book should be coherent and contain all the essential steps needed to perform the analyses covered, such that it can be used for self-study or as a course script. -While we show the R-code behind most of the analyses, we sometimes choose not to show all the code in the html version of the book. This is particularly the case for some of the illustrations. An intrested reader can always consult the [public GitHub repository](https://github.com/TobiasRoth/BDAEcology) with the rmarkdown-files that were used to generate the book. +While we show the R-code behind most of the analyses, we sometimes choose not to show all the code in the html version of the book, e.g. for illustrations. An interested reader can always consult the [public GitHub repository](https://github.com/TobiasRoth/BDAEcology) with the R Markdown files that were used to generate the book. ## How to contribute? {-} -It is open so that everybody with a [GitHub](https://github.com) account can make comments and suggestions for improvement. Readers can contribute in two ways. One way is to add an [issue](https://github.com/TobiasRoth/BDAEcology/issues). The second way is to contribute content directly through the edit button at the top of the page (i.e. a symbol showing a pencil in a square). That button is linked to the rmarkdown source file of each page. You can correct typos or add new text and then submit a [GitHub pull request](https://help.github.com/articles/about-pull-requests/). We try to respond to you as quickly as possible. We are looking forward to your contribution! +This e-book is open so that everybody with a [GitHub](https://github.com) account can make comments and suggestions for improvement. Readers can contribute in two ways. One way is to add an [issue](https://github.com/TobiasRoth/BDAEcology/issues). The second way is to contribute content directly through the edit button at the top of the page (i.e. a symbol showing a pencil in a square). That button is linked to the R Markdown source file of each page. You can correct typos or add new text and then submit a [GitHub pull request](https://help.github.com/articles/about-pull-requests/). We try to respond to you as quickly as possible. We are looking forward to your contribution! ## Acknowledgments {-} -We thank *Yihui Xie* for providing [bookdown](bhttps://bookdown.org/yihui/bookdown/) which makes it much fun to write open books such as ours. -We thank many anonymous students and collaborators who searched information on new software, reported updates and gave feedback on earlier versions of the book. Specifically, we thank Carole Niffenegger for looking up the difference between the bulk and tail ESS in the brm output, Martin Küblbeck for using the conditional logistic regression in rstanarm, ... +We thank the amazing community of people behind the open source [R project](https://cran.r-project.org/). Among the packages we use most are arm [@Gelman2022], rstanarm [@Goodrich2023], rstan [mc-stan.org](https://mc-stan.org) and brms [@Burkner2017], and [bookdown](https://bookdown.org/yihui/bookdown/) to write this book. +We thank our students, collaborators and collegues who introduced us to new techniques and software, reported updates and gave feedback on earlier versions of the book. Among many others, we thank Carole Niffenegger, Martin Küblbeck, Ruben Garcia, [... to be continued]