PIE: A Partially Interpretable Model with Black-Box Refinement (original) (raw)
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 |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | PIE citation info |
| CRAN checks: | PIE results |
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