Merge branch 'master' into model-methods

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

@@ -72,7 +72,7 @@ jobs:
 
       - name: Cache R packages
         if: runner.os != 'Windows'
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           path: ${{ env.R_LIBS_USER }}
           key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}

.github/workflows/Test-coverage.yaml

@@ -50,7 +50,7 @@ jobs:
         shell: Rscript {0}
 
       - name: Cache R packages
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           path: ${{ env.R_LIBS_USER }}
           key: ubuntu-r-4.0-1-${{ hashFiles('.github/depends.Rds') }}

.github/workflows/cmdstan-tarball-check.yaml

@@ -56,7 +56,7 @@ jobs:
 
       - name: Cache R packages
         if: runner.os != 'Windows'
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           path: ${{ env.R_LIBS_USER }}
           key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}

README.md

@@ -37,7 +37,7 @@ analysis.
 You can install the latest beta release of the **cmdstanr** R package with
 
 ```r
-# we recommend running this is a fresh R session or restarting your current session
+# we recommend running this in a fresh R session or restarting your current session
 install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
 ```
 This does not install the vignettes, which take a long time to build, but they are always available

tests/testthat/test-model-laplace.R

@@ -55,8 +55,7 @@ test_that("laplace() runs when all arguments specified validly", {
   expect_equal(fit1$mode()$metadata()$jacobian, as.integer(ok_arg_values$jacobian))
   expect_equal(fit1$mode()$metadata()$init_alpha, ok_arg_values$opt_args$init_alpha)
 
-  # https://github.com/stan-dev/cmdstan/issues/1242
-  #expect_equal(fit1$mode()$metadata()$tol_obj, ok_arg_values$opt_args$tol_obj, tolerance = 0)
+  expect_equal(fit1$mode()$metadata()$tol_obj, ok_arg_values$opt_args$tol_obj, tolerance = 0)
 
   # leaving all at default (except 'data')
   expect_laplace_output(fit2 <- mod$laplace(data = data_list, seed = 123))

tests/testthat/test-model-optimize.R

@@ -117,9 +117,9 @@ test_that("optimize() works with (L-)BFGS tolerances specified", {
       # using values that aren't the defaults
       init_alpha = 0.002,
       tol_obj = 2e-11,
-      tol_rel_obj = 10001,
+      tol_rel_obj = 1000,
       tol_grad = 5e-07,
-      tol_rel_grad = 10000001,
+      tol_rel_grad = 1000000,
       tol_param = 5e-07,
       history_size = 6,
       seed = 123
@@ -128,12 +128,10 @@ test_that("optimize() works with (L-)BFGS tolerances specified", {
   metadata <- fit$metadata()
   expect_equal(metadata$init_alpha, 0.002)
   expect_equal(metadata$tol_obj, 2e-11)
-  expect_equal(metadata$tol_rel_obj, 10001)
-  # https://github.com/stan-dev/cmdstan/issues/1242
-  # expect_equal(metadata$tol_grad, 5e-07)
-  expect_equal(metadata$tol_rel_grad, 10000001)
-  # https://github.com/stan-dev/cmdstan/issues/1242
-  # expect_equal(metadata$tol_param, 5e-07)
+  expect_equal(metadata$tol_rel_obj, 1000)
+  expect_equal(metadata$tol_grad, 5e-07)
+  expect_equal(metadata$tol_rel_grad, 1000000)
+  expect_equal(metadata$tol_param, 5e-07)
   expect_equal(metadata$history_size, 6)
 })
 

vignettes/articles-online-only/opencl.Rmd

@@ -104,7 +104,7 @@ mdata <- list(k = k, n = n, y = y, X = X)
 In this model, most of the computation will be handled by the
 `bernoulli_logit_glm_lpmf` function. Because this is a supported GPU function,
 it should be possible to accelerate it with OpenCL. Check
-[here](http://mc-stan.org/math/d0/d8f/opencl_support.html) for a list of functions
+[here](https://mc-stan.org/math/md_doxygen_2parallelism__support_2opencl__support.html) for a list of functions
 with OpenCL support.
 
 To build the model with OpenCL support, add