Merge pull request #536 from stan-dev/use-github-actions

Add R command check GitHub Action

Author: aloctavodia

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

@@ -0,0 +1,58 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+
+name: R-CMD-check.yaml
+
+permissions: read-all
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: macos-latest,   r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          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::Rcpp
+              any::RcppEigen
+              any::RcppParallel
+              any::StanHeaders
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'

README.Rmd

@@ -16,6 +16,7 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 <!-- [![codecov](https://codecov.io/gh/stan-dev/projpred/branch/master/graph/badge.svg)](https://app.codecov.io/gh/stan-dev/projpred) -->
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/projpred?color=blue)](https://CRAN.R-project.org/package=projpred)
+[![R-CMD-check](https://github.com/stan-dev/projpred/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/stan-dev/projpred/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
 # projpred [<img src="man/figures/logo.svg" align="right" height="139" alt="Stan Logo"/>](https://mc-stan.org)

tests/testthat/test_augdat.R

@@ -211,7 +211,7 @@ test_that(paste(
 
     eta_aug <- refmod_crr$family$linkfun(pref_aug$mu)
     eta_trad <- refmod_crr_trad$family$linkfun(pref_trad$mu)
-    expect_identical(structure(unclass(eta_aug), ndiscrete = NULL), eta_trad,
+    expect_equal(structure(unclass(eta_aug), ndiscrete = NULL), eta_trad,
                      info = tstsetup)
   }
   if (exists("rng_old")) assign(".Random.seed", rng_old, envir = .GlobalEnv)

vignettes/projpred.Rmd

@@ -189,7 +189,8 @@ For this final `cv_varsel()` run (with `validate_search = TRUE`, as recommended)
 In practice, we recommend using either the default of `cv_method = "LOO"` (possibly subsampled, see argument `nloo` of `cv_varsel()`) or a larger value for `K` if this is possible in terms of computation time.
 Here, we also perform the $K$ reference model refits outside of `cv_varsel()`.
 Although not strictly necessary here, this is helpful in practice because often, `cv_varsel()` needs to be re-run multiple times in order to try out different argument settings.
-We also illustrate how **projpred**'s CV (i.e., the CV comprising search and performance evaluation, after refitting the reference model $K$ times) can be parallelized, even though this is of little use here (we have only `K = 2` folds and the fold-wise searches and performance evaluations are quite fast, so the parallelization overhead eats up any runtime improvements).
+We also show how **projpred**'s CV (i.e., the CV comprising search and performance evaluation, after refitting the reference model $K$ times) can be parallelized (though we disable it here for vignette building).
+In practice, parallelization is of little use with only `K = 2` folds and fast fold-wise operations, as the parallelization overhead eats up any runtime improvements.
 Note that for this final `cv_varsel()` run, we cannot make use of `cv_varsel.vsel()` (by applying `cv_varsel()` to `cvvs_fast` or `cvvs_fast_refit` instead of `refm_obj`) because of the change in `nterms_max` (here: 9, for `cvvs_fast` and `cvvs_fast_refit`: 20).
 ```{r cv_varsel, message=FALSE}
 # Refit the reference model K times:
@@ -200,7 +201,11 @@ cv_fits <- run_cvfun(
   ###
 )
 # For running projpred's CV in parallel (see cv_varsel()'s argument `parallel`):
-doParallel::registerDoParallel(ncores)
+# Note: Parallel processing is disabled during package building to avoid issues
+use_parallel <- FALSE  # Set to TRUE for actual parallel processing
+if (use_parallel) {
+  doParallel::registerDoParallel(ncores)
+}
 # Final cv_varsel() run:
 cvvs <- cv_varsel(
   refm_obj,
@@ -211,14 +216,16 @@ cvvs <- cv_varsel(
   nclusters_pred = 20,
   ###
   nterms_max = 9,
-  parallel = TRUE,
+  parallel = use_parallel,
   ### In interactive use, we recommend not to deactivate the verbose mode:
   verbose = 0
   ### 
 )
 # Tear down the CV parallelization setup:
-doParallel::stopImplicitCluster()
-foreach::registerDoSEQ()
+if (use_parallel) {
+  doParallel::stopImplicitCluster()
+  foreach::registerDoSEQ()
+}
 ```
 
 #### Predictive performance plot from final `cv_varsel()` run {#plotfinal}