knitr, rmarkdown, testthat, vcr (>= 0.5.0), ISOcodes, mockery

Random Recursive Partitiong method to match, missing data imputation and nonparametric classification

Random Recursive Partitiong method to match, missing data imputation and nonparametric classification and prediction

Random Recursive Partitiong and Rank-based proximities for data matching, missing data imputation and nonparametric classification and prediction

c(
person(given = "Kun", family = "Chen",
email = "kun.chen@uconn.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-3579-5467")),
person(given = "Wenjie", family = "Wang", role = "ctb",
comment = c(ORCID = "0000-0003-0363-3180")),
person(given = "Jun", family = "Yan", role = "ctb",
comment = c(ORCID = "0000-0003-4401-7296"))
)
Multivariate regression methodologies including
reduced-rank regression (RRR) proposed by
Chen et al. (2013) <doi:10.1093/biomet/ast036>,
reduced-rank ridge regression (RRS) proposed by
Mukherjee and Zhu (2011) <doi:10.1002/sam.10138>,
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>.
ggplot2, glmnet, lassoshooting, MASS, Rcpp (>= 0.12.0)

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>.