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: 0.1.0
Depends: R (≥ 2.10)
Imports: tune (≥ 0.1.2), dplyr (≥ 1.0.0), rlang (≥ 0.4.0), tibble (≥ 2.1.3), purrr (≥ 0.3.2), parsnip (≥ 0.0.4), workflows (≥ 0.1.0), recipes (≥ 0.1.15), yardstick, tidyr, dials, digest, glue, ggplot2, glmnet, rsample, cli, butcher, stats, foreach, generics
Suggests: testthat, covr, kernlab, knitr, modeldata, rmarkdown, palmerpenguins, ranger, nnet, kknn
Published: 2020-11-23
Author: Simon Couch [aut, cre], Max Kuhn [aut], RStudio [cph]
Maintainer: Simon Couch <simonpatrickcouch at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: stacks results

Downloads:

Reference manual: stacks.pdf
Vignettes: Getting Started With stacks
Classification Models With stacks
Package source: stacks_0.1.0.tar.gz
Windows binaries: r-devel: stacks_0.1.0.zip, r-release: stacks_0.1.0.zip, r-oldrel: stacks_0.1.0.zip
macOS binaries: r-release: stacks_0.1.0.tgz, r-oldrel: stacks_0.1.0.tgz

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