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.

Version: 1.0.2
Depends: R (≥ 3.5)
Imports: butcher (≥ 0.1.3), cli, dplyr (≥ 1.1.0), foreach, generics, ggplot2, glmnet, glue, parsnip (≥ 1.0.2), purrr (≥ 1.0.0), recipes (≥ 0.2.0), rlang (≥ 0.4.0), rsample (≥ 0.1.1), stats, tibble (≥ 2.1.3), tidyr, tune (≥ 0.1.3), vctrs (≥ 0.6.1), workflows (≥ 0.2.3), yardstick (≥ 1.1.0)
Suggests: covr, h2o, kernlab, kknn, knitr, mockr, modeldata, nnet, ranger, rmarkdown, SuperLearner, testthat (≥ 3.0.0), workflowsets (≥ 0.1.0)
Published: 2023-04-20
Author: Simon Couch [aut, cre], Max Kuhn [aut], Posit Software, PBC [cph, fnd]
Maintainer: Simon Couch <simon.couch at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: stacks results


Reference manual: stacks.pdf
Vignettes: Getting Started With stacks
Classification Models With stacks


Package source: stacks_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): stacks_1.0.2.tgz, r-oldrel (arm64): stacks_1.0.2.tgz, r-release (x86_64): stacks_1.0.2.tgz, r-oldrel (x86_64): stacks_1.0.2.tgz
Old sources: stacks archive

Reverse dependencies:

Reverse suggests: bundle, DALEXtra, vetiver


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