PIE: A Partially Interpretable Model with Black-Box Refinement
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 |
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 |
Documentation:
Downloads:
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