xgboost notes about sample_prop

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

@@ -71,9 +71,6 @@ jobs:
           Rscript -e "reticulate::conda_create('r-reticulate', packages = 'python==3.6.9')"
           Rscript -e "tensorflow::install_tensorflow(version='1.14.0')"
 
-      - name: Check
-        run: Rscript -e "rcmdcheck::rcmdcheck(args = '--no-manual', error_on = 'warning', check_dir = 'check')"
-
       - name: Session info
         run: |
           options(width = 100)

.github/workflows/pkgdown.yaml

@@ -0,0 +1,46 @@
+on:
+  push:
+    branches: master
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: macOS-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v2
+
+      - uses: r-lib/actions/setup-r@master
+
+      - uses: r-lib/actions/setup-pandoc@master
+
+      - name: Query dependencies
+        run: |
+          install.packages('remotes')
+          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
+          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
+        shell: Rscript {0}
+
+      - name: Cache R packages
+        uses: actions/cache@v1
+        with:
+          path: ${{ env.R_LIBS_USER }}
+          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
+          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
+
+      - name: Install dependencies
+        run: |
+          install.packages("remotes")
+          remotes::install_deps(dependencies = TRUE)
+          remotes::install_dev("pkgdown")
+          remotes::install_github("tidyverse/tidytemplate")
+        shell: Rscript {0}
+
+      - name: Install package
+        run: R CMD INSTALL .
+
+      - name: Deploy package
+        run: pkgdown::deploy_to_branch(new_process = FALSE)
+        shell: Rscript {0}

R/linear_reg.R

@@ -7,8 +7,8 @@
 #' \itemize{
 #'   \item \code{penalty}: The total amount of regularization
 #'  in the model. Note that this must be zero for some engines.
-#'   \item \code{mixture}: The proportion of L1 regularization in
-#'  the model. Note that this will be ignored for some engines.
+#'   \item \code{mixture}: The mixture amounts of different types of
+#'   regularization (see below). Note that this will be ignored for some engines.
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
@@ -23,11 +23,11 @@
 #'  amount of regularization (`glmnet`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization
 #'  (aka weight decay) while the other models can be a combination
-#'  of L1 and L2 (depending on the value of `mixture`).
-#' @param mixture A number between zero and one (inclusive) that
-#'  represents the proportion of regularization that is used for the
-#'  L2 penalty (i.e. weight decay, or ridge regression) versus L1
-#'  (the lasso) (`glmnet` and `spark` only).
+#'  of L1 and L2 (depending on the value of `mixture`; see below).
+#' @param mixture A number between zero and one (inclusive) that is the
+#'  proportion of L1 regularization (i.e. lasso) in the model. When
+#'  `mixture = 1`, it is a pure lasso model while `mixture = 0` indicates that
+#'  ridge regression is being used. (`glmnet` and `spark` only).
 #' @details
 #' The data given to the function are not saved and are only used
 #'  to determine the _mode_ of the model. For `linear_reg()`, the

R/logistic_reg.R

@@ -7,8 +7,8 @@
 #' \itemize{
 #'   \item \code{penalty}: The total amount of regularization
 #'  in the model. Note that this must be zero for some engines.
-#'   \item \code{mixture}: The proportion of L1 regularization in
-#'  the model. Note that this will be ignored for some engines.
+#'   \item \code{mixture}: The mixture amounts of different types of
+#'   regularization (see below). Note that this will be ignored for some engines.
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
@@ -24,10 +24,10 @@
 #'  For `keras` models, this corresponds to purely L2 regularization
 #'  (aka weight decay) while the other models can be a combination
 #'  of L1 and L2 (depending on the value of `mixture`).
-#' @param mixture A number between zero and one (inclusive) that
-#'  represents the proportion of regularization that is used for the
-#'  L2 penalty (i.e. weight decay, or ridge regression) versus L1
-#'  (the lasso) (`glmnet` and `spark` only).
+#' @param mixture A number between zero and one (inclusive) that is the
+#'  proportion of L1 regularization (i.e. lasso) in the model. When
+#'  `mixture = 1`, it is a pure lasso model while `mixture = 0` indicates that
+#'  ridge regression is being used. (`glmnet` and `spark` only).
 #' @details
 #' For `logistic_reg()`, the mode will always be "classification".
 #'

R/multinom_reg.R

@@ -7,8 +7,8 @@
 #' \itemize{
 #'   \item \code{penalty}: The total amount of regularization
 #'  in the model. Note that this must be zero for some engines.
-#'   \item \code{mixture}: The proportion of L1 regularization in
-#'  the model. Note that this will be ignored for some engines.
+#'   \item \code{mixture}: The mixture amounts of different types of
+#'   regularization (see below). Note that this will be ignored for some engines.
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
@@ -24,10 +24,10 @@
 #'  For `keras` models, this corresponds to purely L2 regularization
 #'  (aka weight decay) while the other models can be a combination
 #'  of L1 and L2 (depending on the value of `mixture`).
-#' @param mixture A number between zero and one (inclusive) that
-#'  represents the proportion of regularization that is used for the
-#'  L2 penalty (i.e. weight decay, or ridge regression) versus L1
-#'  (the lasso) (`glmnet` only).
+#' @param mixture A number between zero and one (inclusive) that is the
+#'  proportion of L1 regularization (i.e. lasso) in the model. When
+#'  `mixture = 1`, it is a pure lasso model while `mixture = 0` indicates that
+#'  ridge regression is being used. (`glmnet` and `spark` only).
 #' @details
 #' For `multinom_reg()`, the mode will always be "classification".
 #'

_pkgdown.yml

@@ -3,8 +3,9 @@ url: https://parsnip.tidymodels.org
 template:
   package: tidytemplate
   params:
+    theme: tidymodels
     part_of: <a href="https://tidymodels.org">tidymodels</a>
-    footer: <code>parsnip</code> is a part of the <strong>tidymodels</strong> ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.
+    footer: parsnip is a part of the <strong>tidymodels</strong> ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy.
 
 # https://github.com/tidyverse/tidytemplate for css