tidymodels · topepo · Feb 12, 2025 · Feb 12, 2025 · Feb 12, 2025 · Feb 12, 2025
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
@@ -49,6 +49,11 @@ jobs:
           tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
         shell: Rscript {0}
 
+      - name: Install Torch
+        run: |
+          torch::install_torch()
+        shell: Rscript {0}
+
       - name: Build site
         run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
         shell: Rscript {0}

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,11 +1,11 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.2.1.9004
+Version: 1.3.0
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),
     person("Emil", "Hvitfeldt", , "[email protected]", role = "ctb"),
-    person("Posit Software, PBC", role = c("cph", "fnd"), comment = c(ROR = "03wc8by49"))
+    person("Posit Software, PBC", role = c("cph", "fnd"))
   )
 Maintainer: Max Kuhn <[email protected]>
 Description: A common interface is provided to allow users to specify a
@@ -70,10 +70,10 @@ Suggests:
 VignetteBuilder: 
     knitr
 ByteCompile: true
-Config/Needs/website: C50, dbarts, earth, glmnet, keras, kernlab, kknn,
-    LiblineaR, mgcv, nnet, parsnip, quantreg, randomForest, ranger, rpart, 
-    rstanarm, tidymodels/tidymodels, tidyverse/tidytemplate, rstudio/reticulate,
-    xgboost, rmarkdown
+Config/Needs/website: brulee, C50, dbarts, earth, glmnet, keras, kernlab,
+    kknn, LiblineaR, mgcv, nnet, parsnip, quantreg, randomForest, ranger,
+    rpart, rstanarm, tidymodels/tidymodels, tidyverse/tidytemplate,
+    rstudio/reticulate, xgboost, rmarkdown
 Config/rcmdcheck/ignore-inconsequential-notes: true
 Config/testthat/edition: 3
 Encoding: UTF-8

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# parsnip (development version)
+# parsnip 1.3.0
 
 ## New Features
 
@@ -7,16 +7,17 @@
   * Predictions are encoded via a custom vector type. See [hardhat::quantile_pred()].
   * Predicted quantile levels are designated when the new mode is specified. See `?set_mode`.
 
-* `fit_xy()` can now take dgCMatrix input for `x` argument (#1121).
-
-* `fit_xy()` can now take sparse tibbles as data values (#1165).
-
-* `predict()` can now take dgCMatrix and sparse tibble input for `new_data` argument, and error informatively when model doesn't support it (#1167).
+* Updates for sparse data formats:  
+  * `fit_xy()` can now take dgCMatrix input for `x` argument (#1121).
+  * `fit_xy()` can now take sparse tibbles as data values (#1165).
+  * `predict()` can now take dgCMatrix and sparse tibble input for `new_data` argument, and error informatively when model doesn't support it (#1167).
 
 * New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
 
 * `mlp()` with `keras` engine now work for all activation functions currently supported by `keras` (#1127).
 
+* `mlp()` now has a  `brulee_two_layer` engine.
+
 ## Other Changes
 
 * Transitioned package errors and warnings to use cli (#1147 and #1148 by @shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160, #1161, #1081).
@@ -49,7 +50,6 @@
 
 * `NULL` is no longer accepted as an engine (#1242).
 
-
 # parsnip 1.2.1
 
 * Added a missing `tidy()` method for survival analysis glmnet models (#1086).

diff --git a/inst/models.tsv b/inst/models.tsv
@@ -44,6 +44,7 @@
 "discrim_regularized"	"classification"	"klaR"	"discrim"
 "gen_additive_mod"	"classification"	"mgcv"	NA
 "gen_additive_mod"	"regression"	"mgcv"	NA
+"linear_reg"	"quantile regression"	"quantreg"	NA
 "linear_reg"	"regression"	"brulee"	NA
 "linear_reg"	"regression"	"gee"	"multilevelmod"
 "linear_reg"	"regression"	"glm"	NA
@@ -55,7 +56,6 @@
 "linear_reg"	"regression"	"lm"	NA
 "linear_reg"	"regression"	"lme"	"multilevelmod"
 "linear_reg"	"regression"	"lmer"	"multilevelmod"
-"linear_reg"	"quantile regression"	"quantreg"	NA
 "linear_reg"	"regression"	"spark"	NA
 "linear_reg"	"regression"	"stan"	NA
 "linear_reg"	"regression"	"stan_glmer"	"multilevelmod"

diff --git a/man/details_boost_tree_lightgbm.Rd b/man/details_boost_tree_lightgbm.Rd
diff --git a/man/details_decision_tree_partykit.Rd b/man/details_decision_tree_partykit.Rd
diff --git a/man/details_linear_reg_lme.Rd b/man/details_linear_reg_lme.Rd
diff --git a/man/details_rand_forest_aorsf.Rd b/man/details_rand_forest_aorsf.Rd
diff --git a/man/details_rand_forest_partykit.Rd b/man/details_rand_forest_partykit.Rd
diff --git a/man/details_rand_forest_ranger.Rd b/man/details_rand_forest_ranger.Rd
diff --git a/man/details_survival_reg_flexsurv.Rd b/man/details_survival_reg_flexsurv.Rd
diff --git a/man/details_survival_reg_flexsurvspline.Rd b/man/details_survival_reg_flexsurvspline.Rd
diff --git a/man/parsnip-package.Rd b/man/parsnip-package.Rd
diff --git a/man/rmd/boost_tree_lightgbm.md b/man/rmd/boost_tree_lightgbm.md
@@ -133,6 +133,11 @@ To effectively enable bagging, the user would also need to set the `bagging_freq
 
 bonsai quiets much of the logging output from [lightgbm::lgb.train()] by default. With default settings, logged warnings and errors will still be passed on to the user. To print out all logs during training, set `quiet = TRUE`.
 
+## Sparse Data
+
+
+This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the `Matrix` package and sparse tibbles from the `sparsevctrs` package are supported. See [sparse_data] for more information.
+
 ## Examples 
 
 The "Introduction to bonsai" article contains [examples](https://bonsai.tidymodels.org/articles/bonsai.html) of `boost_tree()` with the `"lightgbm"` engine.

diff --git a/man/rmd/decision_tree_partykit.md b/man/rmd/decision_tree_partykit.md
@@ -1,18 +1,18 @@
 
 
 
-For this engine, there are multiple modes: regression, classification, and censored regression
+For this engine, there are multiple modes: censored regression, regression, and classification
 
 ## Tuning Parameters
 
 
 
 This model has 2 tuning parameters:
 
-- `min_n`: Minimal Node Size (type: integer, default: 20L)
-
 - `tree_depth`: Tree Depth (type: integer, default: see below)
 
+- `min_n`: Minimal Node Size (type: integer, default: 20L)
+
 The `tree_depth` parameter defaults to `0` which means no restrictions are applied to tree depth.
 
 An engine-specific parameter for this model is: 

diff --git a/man/rmd/linear_reg_lme.md b/man/rmd/linear_reg_lme.md
@@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

diff --git a/man/rmd/rand_forest_aorsf.md b/man/rmd/rand_forest_aorsf.md
@@ -1,20 +1,20 @@
 
 
 
-For this engine, there are multiple modes: classification, regression, and censored regression
+For this engine, there are multiple modes: censored regression, classification, and regression
 
 ## Tuning Parameters
 
 
 
 This model has 3 tuning parameters:
 
-- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))
-
 - `trees`: # Trees (type: integer, default: 500L)
 
 - `min_n`: Minimal Node Size (type: integer, default: 5L)
 
+- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))
+
 Additionally, this model has one engine-specific tuning parameter:
 
  * `split_min_stat`: Minimum test statistic required to split a node. Defaults are `3.841459` for censored regression (which is roughly a p-value of 0.05) and `0` for classification and regression. For classification, this tuning parameter should be between 0 and 1, and for regression it should be greater than or equal to 0. Higher values of this parameter cause trees grown by `aorsf` to have less depth.

diff --git a/man/rmd/rand_forest_partykit.md b/man/rmd/rand_forest_partykit.md
@@ -1,20 +1,20 @@
 
 
 
-For this engine, there are multiple modes: regression, classification, and censored regression
+For this engine, there are multiple modes: censored regression, regression, and classification
 
 ## Tuning Parameters
 
 
 
 This model has 3 tuning parameters:
 
+- `trees`: # Trees (type: integer, default: 500L)
+
 - `min_n`: Minimal Node Size (type: integer, default: 20L)
 
 - `mtry`: # Randomly Selected Predictors (type: integer, default: 5L)
 
-- `trees`: # Trees (type: integer, default: 500L)
-
 ## Translation from parsnip to the original package (regression)
 
 The **bonsai** extension package is required to fit this model.

diff --git a/man/rmd/rand_forest_ranger.md b/man/rmd/rand_forest_ranger.md
@@ -108,6 +108,8 @@ The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that e
 
 This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the `Matrix` package and sparse tibbles from the `sparsevctrs` package are supported. See [sparse_data] for more information.
 
+While this engine supports sparse data as an input, it doesn't use it any differently than dense data. Hence there it no reason to convert back and forth.
+
 ## Saving fitted model objects
 
 

diff --git a/man/rmd/survival_reg_flexsurv.Rmd b/man/rmd/survival_reg_flexsurv.Rmd
@@ -40,7 +40,7 @@ survival_reg(dist = character(1)) %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
 
 ```{r child = "template-survival-mean.Rmd"}
 ```

diff --git a/man/rmd/survival_reg_flexsurv.md b/man/rmd/survival_reg_flexsurv.md
@@ -42,7 +42,7 @@ survival_reg(dist = character(1)) %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
 
 
 

diff --git a/man/rmd/survival_reg_flexsurvspline.Rmd b/man/rmd/survival_reg_flexsurvspline.Rmd
@@ -26,7 +26,7 @@ survival_reg() %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
 
 ```{r child = "template-survival-mean.Rmd"}
 ```

diff --git a/man/rmd/survival_reg_flexsurvspline.md b/man/rmd/survival_reg_flexsurvspline.md
@@ -37,7 +37,7 @@ survival_reg() %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
 
 
 

diff --git a/tests/testthat/test-fit_interfaces.R b/tests/testthat/test-fit_interfaces.R
@@ -156,7 +156,7 @@ test_that("overhead of parsnip interface is minimal (#1071)", {
   skip_on_cran()
   skip_on_covr()
   skip_if_not_installed("bench")
-  skip_if_not_installed("parsnip", minimum_version = "1.3.0")
+  skip_if_not_installed("parsnip", minimum_version = "1.4.0")
 
   bm <- bench::mark(
     time_engine = lm(mpg ~ ., mtcars),
Original file line number	Diff line number	Diff line change
Expand Up		@@ -108,6 +108,8 @@ The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that e

		This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the `Matrix` package and sparse tibbles from the `sparsevctrs` package are supported. See [sparse_data] for more information.

		While this engine supports sparse data as an input, it doesn't use it any differently than dense data. Hence there it no reason to convert back and forth.

		## Saving fitted model objects


Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -42,7 +42,7 @@ survival_reg(dist = character(1)) %>%

		The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()].

		For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
		For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.



Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -37,7 +37,7 @@ survival_reg() %>%

		The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()].

		For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
		For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.



Expand Down