shiny (>= 1.6.0), tufte, testthat (>= 3.0.0), digest,
dygraphs, vctrs, tibble, fs, rsconnect, withr (>= 2.4.2), bslib
(>= 0.2.4), sass (>= 0.3.1)

c( person("Stefan", "Widgren", role = c("aut", "cre"),
email = "stefan.widgren@sva.se", comment = "Author of the R
interface to the C-library matio"), person("Christopher",
"Hulbert", role = c("aut"), comment = "Author of the C-library
matio, http://sourceforge.net/projects/matio/"))
rmatio is a package for reading and writing Matlab MAT
files from R. The rmatio package supports reading MAT version 4,
MAT version 5 and MAT compressed version 5. The rmatio package can
write version 5 MAT files and version 5 files with variable
compression.
The package includes the source code of matio written by
Christopher Hulbert (http://sourceforge.net/projects/matio/)
(License: Simplified BSD). The matio io routines have been
adopted to use R printing and error routines.
Stefan Widgren [aut, cre] (Author of the R interface to the C-library
matio),
Christopher Hulbert [aut] (Author of the C-library matio,
http://sourceforge.net/projects/matio/)

c( person("Stefan", "Widgren", role = c("aut", "cre"),
email = "stefan.widgren@gmail.com", comment = "Author of the R
interface to the C-library matio"), person("Christopher",
"Hulbert", role = c("aut"), comment = "Author of the C-library
matio, http://sourceforge.net/projects/matio/"))

Read and write 'Matlab' MAT files from R. The 'rmatio'
package supports reading MAT version 4, MAT version 5 and MAT
compressed version 5. The 'rmatio' package can write version 5 MAT
files and version 5 files with variable compression.

The MCFS-ID Algorithm for Feature Selection and Interdependency
Discovery
c(person('Michal', 'Draminski', role = c('aut', 'cre'), email = 'mdramins@ipipan.waw.pl'),
person('Jacek', 'Koronacki', role = c('aut'), email = 'jacek.koronacki@ipipan.waw.pl'),
person('Julian', 'Zubek', role = c('ctb'), email = 'j.zubek@phd.ipipan.waw.pl'))
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a
Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data.
Michal Draminski [aut, cre],
Jacek Koronacki [aut],
Julian Zubek [ctb]