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Download Rstan Package For R

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Introduction

When you fit a model with brms, the package calls Rstan which is an R interface to the statistical programming language Stan.

The nice thing about brms is that it uses a syntax for specifying model formulae that is based on the syntax of the commonly known lme4 package. The lme4-like syntax of brms is converted into Stan code automatically, so you won't have to learn Stan.1

Stan is built in the programming language C++ and models have to be compiled using C++ to be run. This is all taken care of by brms, so you just need to run brm(...) and brms will do its magic.

However, since the models have to be compiled in C++, you need to set up your computer so that it can use C++. This has to be done only once, before installing brms, and the procedure depends on your operating system. Continue reading for detailed instructions.

Getting help

If you get stuck at any point in the installation process, please contact us at learnb4ss@gmail.com as soon as possible. It is of out-most importance that by the start of the workshop everything works fine on your computer , so we very strongly recommend to go through the installation process as early as possible (e.g. at least two weeks prior to the workshop).

R and RStudio

⚠️ IMPORTANT! We very strongly recommend you to update R to 4.x and RStudio to the latest release. This combo will ensure a smoother installation process and use. We will not be able to provide support for R versions earlier than 4.0.

To install/update R, find the installation disk depending on your operating system here: https://cloud.r-project.org. (If you have macOS with an M1 chip, make sure to install R 4.1.0 for arm64)

We also highly recommend installing RStudio, as we will provide you with interactive RMarkdown files as additional resources that are best viewed and used in RStudio. Please, download RStudio or update it to the latest version: https://www.rstudio.com/products/rstudio/download/#download.

The following sections explain how to configure and install further software that will be necessary for workshop activities. The instructions vary depending on your operating system, so check out the section that is relevant to you. You will perform the following steps:

  1. Configure the C++ toolchain (needed for Stan).
  2. Install Rstan (and Stan).
  3. Install brms.

If you have already installed brms and its dependencies before, then you can skip the installation and just check that everything works by running example(stan_model, package = "rstan", run.dontrun = TRUE) in the console.

macOS

Install Rstan

Just in case you previously tried to install Rstan without success, run the following code to clean installations and configuration files.

Restart R.

Assuming you are using R 4.0 or later, just run in the R console:

Now, verify the installation by running:

                                      example                    (                    stan_model, package                    =                    "rstan", run.dontrun                    =                    TRUE                    )

If the model compiles and starts sampling like in the gif below, you are set.

In case you get the following warning, it is safe to ignore it.

                                      # Warning message:                    # In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :                    #  'C:/rtools40/usr/mingw_/bin/g++' not found

Install brms

Install brms from CRAN simply with:

🎉 Congrats! You are now ready to run Bayesian regressions.

Windows

Install Rstan

Just in case you previously tried to install Rstan without success, run the following code to clean installations and configuration files.

Restart R.

Then just run:

                                      install.packages                    (                    "rstan", repos                    =                    "https://cloud.r-project.org/", dependencies                    =                    TRUE                    )

Now, verify the installation by running:

                                      example                    (                    stan_model, package                    =                    "rstan", run.dontrun                    =                    TRUE                    )

If the model compiles and starts sampling like in the gif below, you are set.

In case you get the following warning, it is safe to ignore it.

                                      # Warning message:                    # In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :                    #  'C:/rtools40/usr/mingw_/bin/g++' not found

Install brms

Install brms from CRAN simply with:

🎉 Congrats! You are now ready to run Bayesian regressions.

Linux

Install brms

Install brms from CRAN simply with:

🎉 Congrats! You are now ready to run Bayesian regressions.

Posted by: hotspotinn.blogspot.com

Source: https://learnb4ss.github.io/learnB4SS/articles/install-brms.html