This package is considered a duplicate. The official version of this package is found at:https://misstiny.r-universe.dev/PIE

Package: PIE 1.0.0

Jingyi Yang

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>.

Authors:Tong Wang [aut], Jingyi Yang [aut, cre], Yunyi Li [aut], Boxiang Wang [aut]

PIE_1.0.0.tar.gz
PIE_1.0.0.tar.gz(r-4.5-noble)PIE_1.0.0.tar.gz(r-4.4-noble)
PIE_1.0.0.tgz(r-4.4-emscripten)PIE_1.0.0.tgz(r-4.3-emscripten)
PIE.pdf |PIE.html✨
PIE/json (API)

# Install 'PIE' in R:

install.packages('PIE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

winequality - Wine Quality Data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 5 exports 6 dependencies

Last updated 25 days agofrom:ca2c6b06d6. Checks:2 OK. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Jan 27 2025
R-4.5-linux	OK	Jan 27 2025

Exports:data_process MAE PIE_fit RPE sparsity_count

Dependencies:data.table gglasso jsonlite lattice Matrix xgboost

Introduction to PIE -- A Partially Interpretable Model with Black-box Refinement

Tong Wang, Jingyi Yang, Yunyi Li and Boxiang Wang

Rendered fromPIE.Rmdusingknitr::rmarkdownon Jan 27 2025.

Last update: 2025-01-27
Started: 2025-01-27

Citation

Readme and manuals

Help Manual

Help page	Topics
data_process: process tabular data into the format for the PIE model.	data_process
MAE: Mean Absolute Error	MAE
PIE: A Partially Interpretable Model with Black-box Refinement	PIE-package PIE
PIE: Partially Interpretable Model	PIE_fit
Make Predictions for PIE	predict.PIE
RPE: Relative Prediction Error	RPE
sparsity_count	sparsity_count
Wine Quality Data	winequality