A Toolbox for Geometric Data Analysis and More
FactoMineR, nleqslv, nnet, ggplot2, ggrepel, RColorBrewer,
rlang, moreparty
Contains functions for 'specific' Multiple Correspondence Analysis,
Class Specific Analysis, Multiple Factor Analysis, 'standardized' MCA, computing and plotting structuring factors and concentration ellipses,
inductive tests and others tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>). It also provides functions
for the translation of logit models coefficients into percentages (Deauvieau (2010) <doi:10.1177/0759106309352586>), weighted contingency tables, an association
measure for contingency tables ("Percentages of Maximum Deviation from Independence", aka PEM, see Cibois (1993) <doi:10.1177/075910639304000103>) and some tools to measure bivariate associations between variables
(phi, Cram<c3><a9>r V, correlation coefficient, eta-squared...).

MASS, wdm, FactoMineR, nleqslv, nnet, ggplot2, ggrepel,
RColorBrewer, rlang
Contains functions for 'specific' Multiple Correspondence Analysis,
Class Specific Analysis, Multiple Factor Analysis, 'standardized' MCA, computing and plotting structuring factors and concentration ellipses,
inductive tests and others tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>). It also provides functions
for the translation of logit models coefficients into percentages (Deauvieau (2010) <doi:10.1177/0759106309352586>), weighted contingency tables, an association
measure for contingency tables ("Percentages of Maximum Deviation from Independence", aka PEM, see Cibois (1993) <doi:10.1177/075910639304000103>) and some tools to measure bivariate associations between variables
(phi, Cramér V, correlation coefficient, eta-squared...).

MASS, wdm, FactoMineR, nleqslv, nnet, ggplot2, ggrepel,
RColorBrewer, rlang, GGally
rmarkdown, knitr, rmdformats, cluster, WeightedCluster, vcd,
R.rsp
Contains functions for 'specific' Multiple Correspondence Analysis,
Class Specific Analysis, Multiple Factor Analysis, 'standardized' MCA, computing and plotting structuring factors and concentration ellipses,
inductive tests and others tools for Geometric Data Analysis (Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0>). It also provides functions
for the translation of logit models coefficients into percentages (Deauvieau (2010) <doi:10.1177/0759106309352586>), weighted contingency tables, an association
measure for contingency tables ("Percentages of Maximum Deviation from Independence", aka PEM, see Cibois (1993) <doi:10.1177/075910639304000103>) and some tools to measure
and plot bivariate associations between variables
(phi, Cramér V, correlation coefficient, eta-squared...).