NMF, magrittr, methods, bigmemory, Biobase

NMF, MASS, Matrix, RANN, RSpectra, Rtsne, cccd, coRanking,
diffusionMap, energy, fastICA, ggplot2, graphics, igraph,
keras, kernlab, knitr, lle, loe, optimx, pcaL1, pcaPP,
reticulate, rgl, scales, scatterplot3d, stats, tensorflow,
testthat, tidyr, tinytex, umap, vegan

c(
person("Guido", "Kraemer",
email = "guido.kraemer@uni-leipzig.de",
role = c("aut", "cre"))
)

NMF, MASS, Matrix, RANN, RSpectra, Rtsne, cccd, coRanking,
diffusionMap, energy, fastICA, ggplot2, graphics, igraph,
keras, kernlab, knitr, loe, optimx, pcaL1, pcaPP, reticulate,
rgl, scales, scatterplot3d, stats, tensorflow, testthat, tidyr,
tinytex, umap, vegan

c(person(given = "Nicolas",
family = "Frerebeau",
role = c("aut", "cre"),
email = "nicolas.frerebeau@u-bordeaux-montaigne.fr",
comment = c(ORCID = "0000-0001-5759-4944")),
person(given = "Jean-Baptiste",
family = "Fourvel",
role = "ctb",
email = "fourvel@mmsh.univ-aix.fr",
comment = c(ORCID = "0000-0002-1061-4642")),
person(given = "Brice",
family = "Lebrun",
role = "ctb",
email = "brice.lebrun@u-bordeaux-montaigne.fr",
comment = c(ORCID = "0000-0001-7503-8685")))
Simple Principal Components Analysis (PCA) and
Correspondence Analysis (CA) based on the Singular Value Decomposition
(SVD). This package provides S4 classes and methods to compute,
extract, summarize and visualize results of multivariate data
analysis. It also includes methods for partial bootstrap validation
described in Greenacre (1984) <isbn: 978-0-12-299050-2> and Lebart et al.
(2006) <isbn: 978-2-10-049616-7>.
folio, FactoMineR, ggrepel, khroma, testthat (>= 3.0.0),
vdiffr
'AllClasses.R' 'AllGenerics.R' 'bootstrap.R' 'ca.R'
'dimensio-package.R' 'helpers.R' 'joint.R' 'mutators.R' 'pca.R'
'plot.R' 'show.R' 'subset.R' 'summary.R' 'zzz.R'
Nicolas Frerebeau [aut, cre] (<https://orcid.org/0000-0001-5759-4944>),
Jean-Baptiste Fourvel [ctb] (<https://orcid.org/0000-0002-1061-4642>),
Brice Lebrun [ctb] (<https://orcid.org/0000-0001-7503-8685>)

covr, folio, FactoMineR, ggrepel, khroma, testthat (>=
3.0.0), vdiffr