{ "_id": "6a227fedcd65a98ecbd51220", "Package": "rrpack", "Title": "Reduced-Rank Regression", "Version": "0.1-14", "Authors@R": "c(\nperson(given = \"Kun\", family = \"Chen\",\nemail = \"kun.chen@uconn.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-3579-5467\")),\nperson(given = \"Wenjie\", family = \"Wang\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-0363-3180\")),\nperson(given = \"Jun\", family = \"Yan\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-4401-7296\"))\n)", "Description": "Multivariate regression methodologies including classical\nreduced-rank regression (RRR) studied by Anderson (1951)\n and Reinsel and Velu (1998)\n, reduced-rank regression via\nadaptive nuclear norm penalization proposed by Chen et al.\n(2013) and Mukherjee et al. (2015)\n, robust reduced-rank regression\n(R4) proposed by She and Chen (2017)\n, generalized/mixed-response\nreduced-rank regression (mRRR) proposed by Luo et al. (2018)\n, row-sparse reduced-rank\nregression (SRRR) proposed by Chen and Huang (2012)\n, reduced-rank regression\nwith a sparse singular value decomposition (RSSVD) proposed by\nChen et al. (2012) and\nsparse and orthogonal factor regression (SOFAR) proposed by\nUematsu et al. 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