ppmSDR: Penalized Principal Machine for Sufficient Dimension Reduction
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) <doi:10.1214/11-AOS932>, Shin and
Artemiou (2017) <doi:10.1016/j.csda.2016.12.003>, Artemiou, Dong and Shin
(2021) <doi:10.1016/j.patcog.2020.107768> and Breheny and Huang (2015)
<doi:10.1007/s11222-013-9424-2>.
| Version: |
2.0.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats, Matrix, grpreg, energy |
| Suggests: |
MASS, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-06-19 |
| DOI: |
10.32614/CRAN.package.ppmSDR (may not be active yet) |
| Author: |
Jungmin Shin [aut, cre] (Department of Biomedical Informatics, The Ohio
State University),
Seung Jun Shin [aut] (Department of Statistics, Korea University) |
| Maintainer: |
Jungmin Shin <c16267 at gmail.com> |
| BugReports: |
https://github.com/c16267/ppmSDR/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/c16267/ppmSDR |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
ppmSDR results |
Documentation:
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