Robust Sufficient Dimension Reduction [R package rSDR version 1.0.3.0]

rSDR: Robust Sufficient Dimension Reduction

A novel sufficient-dimension reduction method is robust against outliers using alpha-distance covariance and manifold-learning in dimensionality reduction problems. Please refer Hsin-Hsiung Huang, Feng Yu & Teng Zhang (2024) <doi:10.1080/10485252.2024.2313137> for the details.

Version: 1.0.3.0

Imports: ManifoldOptim, methods, Rcpp, rstiefel, scatterplot3d, future, future.apply, ggplot2, ggsci

Suggests: expm, knitr, rmarkdown, Matrix, RcppNumerical, fdm2id

Published: 2025年11月10日

DOI: 10.32614/CRAN.package.rSDR

Author: Sheau-Chiann Chen ORCID iD [aut, cre], Shilin Zhao [aut], Hsin-Hsiung Bill Huang ORCID iD [aut]

Maintainer: Sheau-Chiann Chen <sheau-chiann.chen.1 at vumc.org>

License: GPL (≥ 3)

NeedsCompilation: no

CRAN checks: rSDR results

Documentation:

Reference manual: rSDR.html , rSDR.pdf

Vignettes: rSDR_vignette (source, R code)

Downloads:

Package source: rSDR_1.0.3.0.tar.gz

Windows binaries: r-devel: rSDR_1.0.3.0.zip, r-release: rSDR_1.0.3.0.zip, r-oldrel: rSDR_1.0.3.0.zip

macOS binaries: r-release (arm64): rSDR_1.0.3.0.tgz, r-oldrel (arm64): rSDR_1.0.3.0.tgz, r-release (x86_64): rSDR_1.0.3.0.tgz, r-oldrel (x86_64): rSDR_1.0.3.0.tgz

Old sources: rSDR archive

Linking:

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