Reduced-Rank Regression R package rrpack version 0.1-14 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>. Software Depends: R (>= 3.4.0) Imports: ggplot2, glmnet, MASS, Rcpp (>= 0.12.0) LinkingTo: Rcpp, RcppArmadillo Kun Chen <kun.chen@uconn.edu> Comprehensive R Archive Network (CRAN) Kun Chen [aut, cre] (ORCID: <https://orcid.org/0000-0003-3579-5467>), Wenjie Wang [aut] (ORCID: <https://orcid.org/0000-0003-0363-3180>), Jun Yan [ctb] (ORCID: <https://orcid.org/0000-0003-4401-7296>) GPL (>= 3) 2025-12-07 application/tgz https://CRAN.R-project.org/package=rrpack doi:10.32614/CRAN.package.rrpack