<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>A Partially Interpretable Model with Black-Box Refinement</dc:title>
  <dc:title>R package PIE version 1.0.0</dc:title>
  <dc:description>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) &lt;doi:10.48550/arXiv.2105.02410&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.5.0), gglasso, xgboost</dc:relation>
  <dc:relation>Imports: splines, stats</dc:relation>
  <dc:relation>Suggests: knitr, rmarkdown</dc:relation>
  <dc:creator>Jingyi Yang &lt;jy4057@stern.nyu.edu&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Tong Wang [aut],
  Jingyi Yang [aut, cre],
  Yunyi Li [aut],
  Boxiang Wang [aut]</dc:contributor>
  <dc:rights>GPL-2</dc:rights>
  <dc:date>2025-01-27</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=PIE</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.PIE</dc:identifier>
</oai_dc:dc>
