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 = "aut",
comment = c(ORCID = "0000-0003-0363-3180")),
person(given = "Jun", family = "Yan", role = "ctb",
comment = c(ORCID = "0000-0003-4401-7296"))
)

c(person(given = "Kun", family = "Chen", role = c("aut", "cre"),
email = "kun.chen@uconn.edu"),
person(given = "Wenjie", family = "Wang", role = "ctb"),
person(given = "Jun", family = "Yan", role = "ctb"))
Multivariate regression methodologies including reduced-rank
regression (RRR), reduced-rank ridge regression (RRS), robust reduced-rank
regression (R4), generalized/mixed-response reduced-rank regression (mRRR),
row-sparse reduced-rank regression (SRRR), reduced-rank regression with a
sparse singular value decomposition (RSSVD), and sparse and orthogonal
factor regression (SOFAR).
Kun Chen [aut, cre],
Wenjie Wang [ctb],
Jun Yan [ctb]

person("Chris", "Addy", email = "chris.william.addy@gmail.com", role = c("aut", "cre"))
Reduced-rank regression, diagnostics and graphics.
Rcpp, MASS, magrittr, dplyr, ggplot2, plotly, GGally
utility_functions.R reduce_rank_regression.R pca.R cva.R lda.R
rrr.R data.R

Robust Re-Scaling to Better Recover Latent Effects in Data
person("Gregory", "Hunt", email = "ghunt@wm.edu", role = c("aut", "cre"))
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation.
foreach, parallel, doParallel, DEoptim, abind
knitr, rmarkdown, testthat, ggplot2, reshape2

c(person("Gregory", "Hunt", email = "ghunt@wm.edu", role = c("aut", "cre")),person("Johann", "Gagnon-Bartsch", email = "johanngb@umich.edu", role = c("aut")))
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.