yaml, ggplot2, reshape2, dplyr, igraph (>= 1.0.1)

c(person('Michal', 'Draminski', role = c('aut', 'cre'), email = 'michal.draminski@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. M.Draminski, A.Rada-Iglesias, S.Enroth, C.Wadelius, J. Koronacki, J.Komorowski (2008) <doi:10.1093/bioinformatics/btm486>.

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. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.

yaml, ggplot2, gridExtra, reshape2, dplyr, stringi, igraph,
data.table (>= 1.0.1)

Compute the repeated measures correlation, a statistical technique
for determining the overall within-individual relationship among paired measures
assessed on two or more occasions, first introduced by Bland and Altman (1995).
Includes functions for diagnostics, p-value, effect size with confidence
interval including optional bootstrapping, as well as graphing. Also includes
several example datasets.
stats, grDevices, graphics, pwr, psych, RColorBrewer, mnormt

stats, grDevices, graphics, psych, RColorBrewer
knitr, rmarkdown, plotrix, lme4, ggplot2, merTools, pwr

knitr, rmarkdown, plotrix, ggplot2, lme4, merTools, pwr

Compute the repeated measures correlation, a statistical technique
for determining the overall within-individual relationship among paired measures
assessed on two or more occasions, first introduced by Bland and Altman (1995).
Includes functions for diagnostics, p-value, effect size with confidence
interval including optional bootstrapping, as well as graphing. Also includes
several example datasets. For more details, see Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.