Add step_adjust_linear() for linear adjustment

DESCRIPTION

@@ -61,4 +61,4 @@ Config/testthat/edition: 3
 Config/usethis/last-upkeep: 2025-04-24
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.3
+Config/roxygen2/version: 8.1.0

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(bake,step_adjust_linear)
 S3method(bake,step_collapse_cart)
 S3method(bake,step_collapse_stringdist)
 S3method(bake,step_discretize_cart)
@@ -14,6 +15,7 @@ S3method(bake,step_pca_sparse_bayes)
 S3method(bake,step_pca_truncated)
 S3method(bake,step_umap)
 S3method(bake,step_woe)
+S3method(prep,step_adjust_linear)
 S3method(prep,step_collapse_cart)
 S3method(prep,step_collapse_stringdist)
 S3method(prep,step_discretize_cart)
@@ -28,6 +30,7 @@ S3method(prep,step_pca_sparse_bayes)
 S3method(prep,step_pca_truncated)
 S3method(prep,step_umap)
 S3method(prep,step_woe)
+S3method(print,step_adjust_linear)
 S3method(print,step_collapse_cart)
 S3method(print,step_collapse_stringdist)
 S3method(print,step_discretize_cart)
@@ -42,6 +45,7 @@ S3method(print,step_pca_sparse_bayes)
 S3method(print,step_pca_truncated)
 S3method(print,step_umap)
 S3method(print,step_woe)
+S3method(required_pkgs,step_adjust_linear)
 S3method(required_pkgs,step_collapse_cart)
 S3method(required_pkgs,step_collapse_stringdist)
 S3method(required_pkgs,step_discretize_cart)
@@ -56,6 +60,7 @@ S3method(required_pkgs,step_pca_sparse_bayes)
 S3method(required_pkgs,step_pca_truncated)
 S3method(required_pkgs,step_umap)
 S3method(required_pkgs,step_woe)
+S3method(tidy,step_adjust_linear)
 S3method(tidy,step_collapse_cart)
 S3method(tidy,step_collapse_stringdist)
 S3method(tidy,step_discretize_cart)
@@ -82,6 +87,7 @@ export(add_woe)
 export(dictionary)
 export(embed_control)
 export(required_pkgs)
+export(step_adjust_linear)
 export(step_collapse_cart)
 export(step_collapse_stringdist)
 export(step_discretize_cart)
@@ -101,39 +107,51 @@ export(tidy)
 export(tunable)
 import(recipes)
 import(rlang)
-importFrom(dplyr,arrange)
-importFrom(dplyr,bind_rows)
-importFrom(dplyr,contains)
-importFrom(dplyr,ends_with)
-importFrom(dplyr,filter)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,one_of)
-importFrom(generics,required_pkgs)
-importFrom(generics,tidy)
-importFrom(generics,tunable)
+importFrom(dplyr,
+  arrange,
+  bind_rows,
+  contains,
+  ends_with,
+  filter,
+  left_join,
+  mutate,
+  one_of
+)
+importFrom(generics,
+  required_pkgs,
+  tidy,
+  tunable
+)
 importFrom(glue,glue)
 importFrom(lifecycle,deprecated)
 importFrom(recipes,remove_original_cols)
-importFrom(stats,as.formula)
-importFrom(stats,binomial)
-importFrom(stats,coef)
-importFrom(stats,complete.cases)
-importFrom(stats,gaussian)
-importFrom(stats,glm)
-importFrom(stats,model.matrix)
-importFrom(stats,na.omit)
-importFrom(stats,setNames)
-importFrom(tibble,as_tibble)
-importFrom(tibble,rownames_to_column)
-importFrom(tibble,tibble)
+importFrom(stats,
+  as.formula,
+  binomial,
+  coef,
+  complete.cases,
+  gaussian,
+  glm,
+  model.matrix,
+  na.omit,
+  setNames
+)
+importFrom(tibble,
+  as_tibble,
+  rownames_to_column,
+  tibble
+)
 importFrom(tidyr,gather)
-importFrom(utils,capture.output)
-importFrom(utils,compareVersion)
-importFrom(utils,globalVariables)
-importFrom(utils,packageVersion)
-importFrom(utils,stack)
-importFrom(uwot,umap)
-importFrom(uwot,umap_transform)
+importFrom(utils,
+  capture.output,
+  compareVersion,
+  globalVariables,
+  packageVersion,
+  stack
+)
+importFrom(uwot,
+  umap,
+  umap_transform
+)
 importFrom(vctrs,vec_cbind)
 importFrom(withr,with_seed)

NEWS.md

@@ -1,5 +1,9 @@
 # embed (development version)
 
+## New Steps
+
+* Added `step_adjust_linear()` for linear residualization of selected outcomes against nuisance variables.
+
 # embed 1.2.2
 
 * Fixed bug on step_umap() where the number of calculated components would be zero. (#271)
@@ -87,7 +91,7 @@
 
 # embed 1.0.0
 
-* `step_collapse_cart()` can pool a predictor's factor levels using a tree-based method. 
+* `step_collapse_cart()` can pool a predictor's factor levels using a tree-based method.
 
 * `step_collapse_stringdist()` can pool a predictor's factor levels using string distances.
 
@@ -109,7 +113,7 @@
 
 * Re-licensed package from GPL-2 to MIT. See [consent from copyright holders here](https://github.com/tidymodels/embed/issues/78).
 
-* The tunable parameter ranges for `step_umap()` were changed for `neighbors`, `num_comp`, and `min_dist` to prevent `uwot` segmentation faults. The step also check to see if the data dimensions are consistent with the argument values. 
+* The tunable parameter ranges for `step_umap()` were changed for `neighbors`, `num_comp`, and `min_dist` to prevent `uwot` segmentation faults. The step also check to see if the data dimensions are consistent with the argument values.
 
 * Two new PCA steps were added, each using sparse techniques for estimation: `step_pca_sparse()` and `step_pca_sparse_bayes()`.
 
@@ -124,78 +128,78 @@
 
 # embed 0.1.4
 
- * Minor release with changes to test for cases when CRAN cannot get `xgboost` to work on their Solaris configuration. 
- 
- * `lme4` and `rstanarm` are now in the Suggests list so they are not automatically installed with `embed`. A message is written to the console if those packages are missing and their associated steps functions are invoked. 
+ * Minor release with changes to test for cases when CRAN cannot get `xgboost` to work on their Solaris configuration.
+
+ * `lme4` and `rstanarm` are now in the Suggests list so they are not automatically installed with `embed`. A message is written to the console if those packages are missing and their associated steps functions are invoked.
 
 # embed 0.1.3
 
- * More changes to enable better parallel processing on windows. 
+ * More changes to enable better parallel processing on windows.
 
 # embed 0.1.2
 
- * Changes to enable better parallel processing on windows. 
+ * Changes to enable better parallel processing on windows.
 
 # embed 0.1.1
 
  * Changes to tests to get out of archive jail.
- 
- * Updated the plumbing behind `step_woe()`. 
- 
- * Due to a bug in `tensorflow`, added a "warm start" to instigate a TF session if one does not currently exist. 
- 
+
+ * Updated the plumbing behind `step_woe()`.
+
+ * Due to a bug in `tensorflow`, added a "warm start" to instigate a TF session if one does not currently exist.
+
 # embed 0.1.0
 
  * Changes for `dplyr` 1.0.0
- 
+
 ## New Steps
-
-  * `step_discretize_xgb()` and `step_discretize_cart()` can be used to convert numeric predictors to categorical using supervised binning methods based on tree models. Thanks to Konrad Semsch for the contribution. 
 
- * Added `step_feature_hash()` for creating dummy variables using feature hashing. 
+  * `step_discretize_xgb()` and `step_discretize_cart()` can be used to convert numeric predictors to categorical using supervised binning methods based on tree models. Thanks to Konrad Semsch for the contribution.
+
+ * Added `step_feature_hash()` for creating dummy variables using feature hashing.
 
 ## Breaking Changes
 
- * `tidy.step_woe()` now has column names consistent with other recipe steps. 
+ * `tidy.step_woe()` now has column names consistent with other recipe steps.
 
 ## Bug fixes
 
- * Fixed a bug in detecting the TF version. 
+ * Fixed a bug in detecting the TF version.
 
 
 # embed 0.0.6
 
-* Small changes for base R's `stringsAsFactors` change. 
+* Small changes for base R's `stringsAsFactors` change.
 
 # `embed` 0.0.5
 
- * The example data are now in the `modeldata` package. 
- 
- * Small TF updates to `step_embed()`. 
+ * The example data are now in the `modeldata` package.
+
+ * Small TF updates to `step_embed()`.
 
 
 # `embed` 0.0.4
 
  * Methods were added for a future generic called `tunable()`. This outlines which parameters in a step can/could be tuned.
 
- * Small updates to work with different versions of `tidyr`.  
- 
+ * Small updates to work with different versions of `tidyr`.
+
 
 # `embed` 0.0.3
 
 ## New Steps
 
- * `step_umap()` was added for both supervised and unsupervised encodings. 
+ * `step_umap()` was added for both supervised and unsupervised encodings.
  * `step_woe()` created weight of evidence encodings.
 
 
 # `embed` 0.0.2
 
-A mostly maintainence release to be compatible with version 0.1.3 of `recipes`. 
+A mostly maintainence release to be compatible with version 0.1.3 of `recipes`.
 
 ## Other Changes:
 
- * The package now depends on the `generics` pacakge to get the `broom` `tidy` methods. 
+ * The package now depends on the `generics` pacakge to get the `broom` `tidy` methods.
 
  * Karim Lahrichi added the ability to use callbacks when fitting tensorflow models. [PR](https://github.com/tidymodels/embed/pull/9)