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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "PIE" in publications use:'
type: software
license: GPL-2.0-only
title: 'PIE: A Partially Interpretable Model with Black-Box Refinement'
version: 1.0.0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.PIE
abstract: 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) <https://doi.org/10.48550/arXiv.2105.02410>.
authors:
- family-names: Wang
  given-names: Tong
- family-names: Yang
  given-names: Jingyi
  email: jy4057@stern.nyu.edu
- family-names: Li
  given-names: Yunyi
- family-names: Wang
  given-names: Boxiang
preferred-citation:
  type: article
  title: 'Partially interpretable estimators (PIE): black-box-refined interpretable
    machine learning'
  authors:
  - family-names: Wang
    given-names: Tong
  - family-names: Yang
    given-names: Jingyi
    email: jy4057@stern.nyu.edu
  - family-names: Li
    given-names: Yunyi
  - family-names: Wang
    given-names: Boxiang
  journal: arXiv preprint arXiv:2105.02410
  year: '2021'
repository: https://misstiny.r-universe.dev
commit: ca2c6b06d64bb0a0a60fee4d3b8ed1dde339784d
date-released: '2025-01-20'
contact:
- family-names: Yang
  given-names: Jingyi
  email: jy4057@stern.nyu.edu