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 posit.co> |
| BugReports: |
https://github.com/tidymodels/stacks/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://stacks.tidymodels.org/,
https://github.com/tidymodels/stacks |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
stacks results |