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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 "ppmSDR" in publications use:'
type: software
license: GPL-3.0-only
title: 'ppmSDR: Penalized Principal Machine for Sufficient Dimension Reduction'
version: 2.0.0
abstract: A unified, computation-friendly framework for penalized principal machines
  (P2M), a class of sparse sufficient dimension reduction (SDR) estimators for regression
  and binary classification. Principal machines (PM) estimate the central subspace
  by solving a family of convex-loss problems over several cutoffs; their penalized
  counterparts (P2M) add a row-group sparsity penalty so that dimension reduction
  and variable selection are performed simultaneously. All estimators are fitted by
  a single group coordinate descent (GCD) algorithm that accommodates least squares,
  logistic, asymmetric least squares, L2-hinge, hinge (support vector machine, SVM)
  and quantile losses, together with the least absolute shrinkage and selection operator
  (LASSO), the smoothly clipped absolute deviation (SCAD) penalty and the minimax
  concave penalty (MCP). Methods are described in Li, Artemiou and Li (2011) <https://doi.org/10.1214/11-AOS932>,
  Shin and Artemiou (2017) <https://doi.org/10.1016/j.csda.2016.12.003>, Artemiou,
  Dong and Shin (2021) <https://doi.org/10.1016/j.patcog.2020.107768> and Breheny
  and Huang (2015) <https://doi.org/10.1007/s11222-013-9424-2>.
authors:
- family-names: Shin
  given-names: Jungmin
  email: c16267@gmail.com
- family-names: Shin
  given-names: Seung Jun
  email: sjshin@korea.ac.kr
repository: https://c16267.r-universe.dev
repository-code: https://github.com/c16267/ppmSDR
commit: 658617698eb144bb2652c3f096c30905ef22bed4
url: https://github.com/c16267/ppmSDR
date-released: '2026-06-13'
contact:
- family-names: Shin
  given-names: Jungmin
  email: c16267@gmail.com