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rrpack: Reduced-Rank Regression

Multivariate regression methodologies including classical reduced-rank regression (RRR) studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>, reduced-rank regression via adaptive nuclear norm penalization proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>, robust reduced-rank regression (R4) proposed by She and Chen (2017) <doi:10.1093/biomet/asx032>, generalized/mixed-response reduced-rank regression (mRRR) proposed by Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>, row-sparse reduced-rank regression (SRRR) proposed by Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>, reduced-rank regression with a sparse singular value decomposition (RSSVD) proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x> and sparse and orthogonal factor regression (SOFAR) proposed by Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.

Version:	0.1-14
Depends:	R (≥ 3.4.0)
Imports:	ggplot2,glmnet,MASS,Rcpp (≥ 0.12.0)
LinkingTo:	Rcpp,RcppArmadillo
Published:	2025-12-07
DOI:	10.32614/CRAN.package.rrpack
Author:	Kun Chen [aut, cre], Wenjie Wang [aut], Jun Yan [ctb]
Maintainer:	Kun Chen <kun.chen at uconn.edu>
License:	GPL (≥ 3)
NeedsCompilation:	yes
CRAN checks:	rrpack results

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