Implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) <doi:10.48550/arXiv.2105.02410>.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0), gglasso, xgboost |
| Imports: | splines, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2025-01-27 |
| DOI: | 10.32614/CRAN.package.PIE |
| Author: | Tong Wang [aut], Jingyi Yang [aut, cre], Yunyi Li [aut], Boxiang Wang [aut] |
| Maintainer: | Jingyi Yang <jy4057 at stern.nyu.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | PIE citation info |
| CRAN checks: | PIE results |
| Reference manual: | PIE.html , PIE.pdf |
| Vignettes: |
Introduction to PIE – A Partially Interpretable Model with Black-box Refinement (source) |
| Package source: | PIE_1.0.0.tar.gz |
| Windows binaries: | r-devel: PIE_1.0.0.zip, r-release: PIE_1.0.0.zip, r-oldrel: PIE_1.0.0.zip |
| macOS binaries: | r-release (arm64): PIE_1.0.0.tgz, r-oldrel (arm64): PIE_1.0.0.tgz, r-release (x86_64): PIE_1.0.0.tgz, r-oldrel (x86_64): PIE_1.0.0.tgz |
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