Package: stacks 1.1.1.9001

Max Kuhn

stacks: Tidy Model Stacking

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.

Authors:Simon Couch [aut], Max Kuhn [aut, cre], Posit Software, PBC [cph, fnd]

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manual.pdf |manual.html✨
card.svg |card.png
stacks/json (API)
NEWS

# Install 'stacks' in R:

install.packages('stacks', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/stacks/issues

Pkgdown/docs site:https://stacks.tidymodels.org

Datasets:

class_folds - Example Objects
class_res_nn - Example Objects
class_res_rf - Example Objects
log_res_nn - Example Objects
log_res_rf - Example Objects
reg_folds - Example Objects
reg_res_lr - Example Objects
reg_res_sp - Example Objects
reg_res_svm - Example Objects
tree_frogs - Tree frog embryo hatching data
tree_frogs_class_test - Example Objects
tree_frogs_reg_test - Example Objects

On CRAN:

12.40 score 302 stars 2 packages 1.1k scripts 5.8k downloads 22 mentions 28 exports 109 dependencies

Last updated from:5be93a6a5a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	230
source / vignettes	OK	349
linux-release-x86_64	OK	196
macos-release-arm64	OK	119
macos-oldrel-arm64	OK	109
windows-devel	OK	170
windows-release	OK	120
windows-oldrel	OK	132
wasm-release	OK	155

Exports:%>%add_candidates augment autoplot axe_call axe_call.model_stack axe_ctrl axe_ctrl.model_stack axe_data axe_data.model_stack axe_env axe_env.model_stack axe_fitted axe_fitted.model_stack blend_predictions build_linear_predictor butcher collect_parameters control_stack_bayes control_stack_grid control_stack_resamples fit_members get_expressions predict.data_stack predict.model_stack prediction_eqn stack_predict stacks

Dependencies:base64enc bslib butcher cachem class cli clock codetools cpp11 crayon data.table diagram dials DiceDesign digest dplyr evaluate farver fastmap fontawesome foreach fs furrr future future.apply GauPro generics ggplot2 glmnet globals glue gower gtable hardhat highr htmltools ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lbfgs lifecycle listenv lobstr lubridate magrittr MASS Matrix memoise mime mixopt modelenv nnet numDeriv parallelly parsnip pillar pkgconfig prettyunits prodlim progressr purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen recipes rlang rmarkdown rpart rsample S7 sass scales sfd shape slider sparsevctrs splitfngr SQUAREM stringi stringr survival tailor tibble tidyr tidyselect timechange timeDate tinytex tune tzdb utf8 vctrs viridisLite warp withr workflows xfun yaml yardstick

Classification Models With stacks

Rendered fromclassification.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-04-25
Started: 2020-07-14

Getting Started With stacks

Rendered frombasics.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-04-25
Started: 2020-07-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add model definitions to a data stack	add_candidates
Augment a model stack	augment.model_stack
Plot results of a stacked ensemble model.	autoplot.linear_stack
Axing a model_stack.	axe_call.model_stack axe_ctrl.model_stack axe_data.model_stack axe_env.model_stack axe_fitted.model_stack axe_model_stack
Determine stacking coefficients from a data stack	blend_predictions
Collect candidate parameters and stacking coefficients	collect_parameters collect_parameters.data_stack collect_parameters.default collect_parameters.model_stack
Control wrappers	control_stack control_stack_bayes control_stack_grid control_stack_resamples
Example Objects	class_folds class_res_nn class_res_rf example_data log_res_nn log_res_rf reg_folds reg_res_lr reg_res_sp reg_res_svm tree_frogs_class_test tree_frogs_reg_test
Fit model stack members with non-zero stacking coefficients	fit_members
Obtain prediction equations for all possible values of type	get_expressions get_expressions._elnet get_expressions._lognet get_expressions._multnet
Predicting with a model stack	predict.data_stack
Predicting with a model stack	predict.model_stack
Initialize a Stack	stacks
stacks: Tidy Model Stacking	stacks-package stacks_description stacks_package
Tree frog embryo hatching data	tree_frogs