Added cmdstanr to GHA

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

@@ -40,12 +40,16 @@ jobs:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
+          extra-repositories: 'https://stan-dev.r-universe.dev'
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::rcmdcheck, any::BH, any::RcppEigen, any::vdiffr
+          extra-packages: any::rcmdcheck, any::BH, any::RcppEigen, any::vdiffr, any::cmdstanr
           needs: check
 
+      - name: Install CmdStan
+        run: Rscript -e "cmdstanr::install_cmdstan()"
+
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true

.github/workflows/test-coverage.yaml

@@ -21,12 +21,16 @@ jobs:
       - uses: r-lib/actions/setup-r@v2
         with:
           use-public-rspm: true
+          extra-repositories: 'https://stan-dev.r-universe.dev'
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::covr, any::xml2, any::vdiffr
+          extra-packages: any::covr, any::xml2, any::vdiffr, any::cmdstanr
           needs: coverage
 
+      - name: Install CmdStan
+        run: Rscript -e "cmdstanr::install_cmdstan()"
+
       - name: Test coverage
         run: |
           cov <- covr::package_coverage(

DESCRIPTION

@@ -42,6 +42,7 @@ Imports:
     tidyselect,
     utils
 Suggests:
+    cmdstanr,
     ggdist,
     ggfortify,
     gridExtra (>= 2.2.1),
@@ -63,3 +64,4 @@ VignetteBuilder: knitr
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
+Additional_repositories: https://stan-dev.r-universe.dev/

vignettes/visual-mcmc-diagnostics.Rmd

@@ -154,7 +154,7 @@ schools_mod_ncp <- stan_model("schools_mod_ncp.stan")
 We then fit the model by calling Stan's MCMC algorithm using the `sampling` 
 function (the increased `adapt_delta` param is to make the sampler a bit more "careful" and avoid false positive divergences),
 ```{r fit-models-hidden, results='hide', message=FALSE}
-fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214055, control = list(adapt_delta = 0.9))
+fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 20251208, control = list(adapt_delta = 0.9))
 fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9))
 ```
 and extract a `iterations x chains x parameters` array of posterior draws with