Merge branch 'main' into sparse-input

Author: topepo

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

@@ -11,7 +11,9 @@ on:
     branches: [main, master]
   workflow_dispatch:
 
-name: R-CMD-check
+name: R-CMD-check.yaml
+
+permissions: read-all
 
 jobs:
   R-CMD-check:
@@ -26,11 +28,15 @@ jobs:
           - {os: macos-latest,   r: 'release'}
 
           - {os: windows-latest, r: 'release'}
+          # use 4.0 or 4.1 to check with rtools40's older compiler
+          - {os: windows-latest, r: 'oldrel-4'}
 
-          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
-          - {os: ubuntu-latest,   r: 'release'}
-          - {os: ubuntu-latest,   r: 'oldrel-1'}
-          # - {os: ubuntu-latest,   r: 'oldrel-2'} # temp until lava pacakge is good
+          - {os: ubuntu-latest,  r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,  r: 'release'}
+          - {os: ubuntu-latest,  r: 'oldrel-1'}
+          - {os: ubuntu-latest,  r: 'oldrel-2'}
+          - {os: ubuntu-latest,  r: 'oldrel-3'}
+          #- {os: ubuntu-latest,  r: 'oldrel-4'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
@@ -40,7 +46,7 @@ jobs:
       CXX14FLAGS: -Wall -g -02
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 

.github/workflows/pkgdown.yaml

@@ -9,7 +9,9 @@ on:
     types: [published]
   workflow_dispatch:
 
-name: pkgdown
+name: pkgdown.yaml
+
+permissions: read-all
 
 jobs:
   pkgdown:
@@ -19,8 +21,10 @@ jobs:
       group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -51,7 +55,7 @@ jobs:
 
       - name: Deploy to GitHub pages 🚀
         if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        uses: JamesIves/github-pages-deploy-action@v4.5.0
         with:
           clean: false
           branch: gh-pages

.github/workflows/pr-commands.yaml

@@ -4,7 +4,9 @@ on:
   issue_comment:
     types: [created]
 
-name: Commands
+name: pr-commands.yaml
+
+permissions: read-all
 
 jobs:
   document:
@@ -13,8 +15,10 @@ jobs:
     runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
@@ -50,8 +54,10 @@ jobs:
     runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:

.github/workflows/test-coverage.yaml

@@ -6,7 +6,9 @@ on:
   pull_request:
     branches: [main, master]
 
-name: test-coverage
+name: test-coverage.yaml
+
+permissions: read-all
 
 jobs:
   test-coverage:
@@ -15,15 +17,15 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
         with:
           use-public-rspm: true
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::covr
+          extra-packages: any::covr, any::xml2
           needs: coverage
 
       - name: Install dev reticulate
@@ -44,23 +46,32 @@ jobs:
 
       - name: Test coverage
         run: |
-          covr::codecov(
+          cov <- covr::package_coverage(
             quiet = FALSE,
             clean = FALSE,
-            install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
           )
+          covr::to_cobertura(cov)
         shell: Rscript {0}
 
+      - uses: codecov/codecov-action@v4
+        with:
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }}
+          file: ./cobertura.xml
+          plugin: noop
+          disable_search: true
+          token: ${{ secrets.CODECOV_TOKEN }}
+
       - name: Show testthat output
         if: always()
         run: |
           ## --------------------------------------------------------------------
-          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
         shell: bash
 
       - name: Upload test results
         if: failure()
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: coverage-test-failures
           path: ${{ runner.temp }}/package

DESCRIPTION

@@ -25,7 +25,7 @@ Imports:
     ggplot2,
     globals,
     glue,
-    hardhat (>= 1.3.1.9000),
+    hardhat (>= 1.4.0),
     lifecycle,
     magrittr,
     pillar,
@@ -80,6 +80,5 @@ Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
 Remotes:  
-    tidymodels/hardhat,
     r-lib/sparsevctrs
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2

NEWS.md

@@ -1,11 +1,13 @@
 # parsnip (development version)
 
-* Aligned `null_model()` with other model types; the model type now has an
-  engine argument that defaults to `"parsnip"` and is checked with the same
-  machinery that checks other model types in the package (#1083).
+
+* `fit_xy()` currently raises an error for `gen_additive_mod()` model specifications as the default engine (`"mgcv"`) specifies smoothing terms in model formulas. However, some engines specify smooths via additional arguments, in which case the restriction on `fit_xy()` is excessive. parsnip will now only raise an error when fitting a `gen_additive_mod()` with `fit_xy()` when using the `"mgcv"` engine (#775).
+
+* Aligned `null_model()` with other model types; the model type now has an engine argument that defaults to `"parsnip"` and is checked with the same machinery that checks other model types in the package (#1083).
 
 * New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
 
+
 # parsnip 1.2.1
 
 * Added a missing `tidy()` method for survival analysis glmnet models (#1086).

R/README.md

@@ -1,24 +1,24 @@
-# Some Notes on the Design of `parsnip`
+# Some Notes on the Design of parsnip
 
-`parsnip` is trying to solve the issues of unified interfaces for the myriad R modeling functions that have very heterogeneous interfaces and return values. It defines a set of modules, which are specific tasks, such as 
+The parsnip package is trying to solve the issues of unified interfaces for the myriad R modeling functions that have very heterogeneous interfaces and return values. It defines a set of modules, which are specific tasks, such as 
 
  * fitting the model
  * obtaining numeric predictions for regression models
  * computing different types of predictions for classification and censored regression models
 
 and so on. The list of modules is likely to grow over time to include variable importance scores and so on,. 
 
-`caret` was written for the same purpose. The approach there was to encapsulate the modules as functions (see [this directory](https://github.com/topepo/caret/tree/master/models/files) for examples). The issue with having these modules as functions are:
+The caret package was written for the same purpose. The approach there was to encapsulate the modules as functions (see [this directory](https://github.com/topepo/caret/tree/master/models/files) for examples). The issue with having these modules as functions are:
 
  * A lot of code duplication.
  * More difficult to maintain.
  * Any functions in open code had to be a dependency of some sort. This led to a long ago version having about 200 package dependencies which was problematic. 
 
-To get around the last point, `caret` _compiles_ these modules into a large list and saves it in the package as an RData file. This avoids `R CMD check` from noticing that code and triggering warnings about dependencies. 
+To get around the last point, caret _compiles_ these modules into a large list and saves it in the package as an RData file. This avoids `R CMD check` from noticing that code and triggering warnings about dependencies. 
 
 ## Model Fitting Modules
 
-`parsnip` approaches the problem differently and relies more on using `call` objects for the modules. In the simple cases, the fit module is a list that contains information about the module including the package and function name for the call as well as any default options. For example, for logistic regression using `glm`, the module may look like:
+parsnip approaches the problem differently and relies more on using `call` objects for the modules. In the simple cases, the fit module is a list that contains information about the module including the package and function name for the call as well as any default options. For example, for logistic regression using `glm`, the module may look like:
 
 ```r
 list(
@@ -77,9 +77,9 @@ The same is true for quosures.
 
 Making predictions is done in a manner similar to fitting models; a call is created in the same way. However, there are additional complexities. 
 
-First, the data or model fit object may require some preprocessing to make the predict function work. This does _not_ include executing a formula method on the data but may include coercing the new data into an appropriate format. It can also be used to check for specific fit object requirements. For example, an additional option is required for the `ranger` package to compute class probabilities. The `pre` element of a prediction module can be used to check that the relevant option is set correctly. 
+First, the data or model fit object may require some preprocessing to make the predict function work. This does _not_ include executing a formula method on the data but may include coercing the new data into an appropriate format. It can also be used to check for specific fit object requirements. For example, an additional option is required for the ranger package to compute class probabilities. The `pre` element of a prediction module can be used to check that the relevant option is set correctly. 
 
-Second, there is a high likelihood that the results of executing the prediction code will require post-processing to put the results into a usable format. `ranger`, for example, returns an object of specific class that contains the predicted values for the new data. The `post` element of the prediction module would extract this value and put it into a more consistent format. 
+Second, there is a high likelihood that the results of executing the prediction code will require post-processing to put the results into a usable format. ranger, for example, returns an object of specific class that contains the predicted values for the new data. The `post` element of the prediction module would extract this value and put it into a more consistent format. 
 
 The postprocessor can also be used to coerce the results into a [_tidy format_](https://tidymodels.github.io/model-implementation-principles/model-predictions.html#return-values). 
 

R/aaa_models.R

@@ -511,7 +511,7 @@ check_interface_val <- function(x) {
 #'   and `raw`.
 #' @param pkg An options character string for a package name.
 #' @param parsnip A single character string for the "harmonized" argument name
-#'  that `parsnip` exposes.
+#'  that parsnip exposes.
 #' @param original A single character string for the argument name that
 #'  underlying model function uses.
 #' @param value A list that conforms to the `fit_obj` or `pred_obj` description
@@ -525,7 +525,7 @@ check_interface_val <- function(x) {
 #' @keywords internal
 #' @details These functions are available for users to add their
 #'  own models or engines (in a package or otherwise) so that they can
-#'  be accessed using `parsnip`. This is more thoroughly documented
+#'  be accessed using parsnip. This is more thoroughly documented
 #'  on the package web site (see references below).
 #'
 #' In short, `parsnip` stores an environment object that contains

R/add_in.R

@@ -1,7 +1,7 @@
 #' Start an RStudio Addin that can write model specifications
 #'
 #' `parsnip_addin()` starts a process in the RStudio IDE Viewer window
-#' that allows users to write code for `parsnip` model specifications from
+#' that allows users to write code for parsnip model specifications from
 #' various R packages. The new code is written to the current document at the
 #' location of the cursor.
 #'

R/engines.R

@@ -72,7 +72,7 @@ load_libs <- function(x, quiet, attach = FALSE) {
 #'
 #' - _Main arguments_ are more commonly used and tend to be available across
 #' engines. These names are standardized to work with different engines in a
-#' consistent way, so you can use the \pkg{parsnip} main argument `trees`,
+#' consistent way, so you can use the parsnip main argument `trees`,
 #' instead of the heterogeneous arguments for this parameter from \pkg{ranger}
 #' and  \pkg{randomForest} packages (`num.trees` and `ntree`, respectively). Set
 #' these in your model type function, like `rand_forest(trees = 2000)`.
@@ -154,10 +154,10 @@ set_engine.default <- function(object, engine, ...) {
 #' Display currently available engines for a model
 #'
 #' The possible engines for a model can depend on what packages are loaded.
-#' Some \pkg{parsnip} extension add engines to existing models. For example,
+#' Some parsnip extension add engines to existing models. For example,
 #' the \pkg{poissonreg} package adds additional engines for the [poisson_reg()]
 #' model and these are not available unless \pkg{poissonreg} is loaded.
-#' @param x The name of a `parsnip` model (e.g., "linear_reg", "mars", etc.)
+#' @param x The name of a parsnip model (e.g., "linear_reg", "mars", etc.)
 #' @return A tibble.
 #'
 #' @examplesIf !parsnip:::is_cran_check()

R/extract.R

@@ -30,7 +30,7 @@
 #'  importance/explainers.
 #'
 #' However, users should not invoke the `predict()` method on an extracted
-#'  model. There may be preprocessing operations that `parsnip` has executed on
+#'  model. There may be preprocessing operations that parsnip has executed on
 #'  the data prior to giving it to the model. Bypassing these can lead to errors
 #'  or silently generating incorrect predictions.
 #'