## MCA

speMCA()

Performs a 'specific' MCA

csMCA()

Performs a 'class specific' MCA

multiMCA()

Performs Multiple Factor Analysis

stMCA()

Performs a 'standardized' MCA

## Interpreting MCA

modif.rate()

Computes Benzecri's modified rates of variance of a MCA

contrib()

Computes contributions for a MCA

tabcontrib()

Displays the categories contributing most to axes for a MCA

dimcontrib()

Describes the contributions to axes for a MCA

dimdescr()

Describes the dimensions of a MCA

dimeta2()

Describes the eta2 of supplementary variables for the axes of a MCA

dimtypicality()

Typicality tests for supplementary variables of a MCA

homog.test()

Computes a homogeneity test for a categorical supplementary variable

varsup()

Computes statistics for a categorical supplementary variable

indsup()

Computes statistics for supplementary individuals

## Plotting MCA

plot(<speMCA>)

Plots 'specific' MCA results

plot(<csMCA>)

Plots 'class specific' MCA results

plot(<multiMCA>)

Plots Multiple Factor Analysis

plot(<stMCA>)

Plots 'standardized' MCA results

textvarsup()

Adds a categorical supplementary variable to a MCA graph

textindsup()

Adds supplementary individuals to a MCA graph

conc.ellipse()

Adds concentration ellipses to a cloud of individuals.

ggcloud_indiv()

Plots MCA cloud of individuals with ggplot2

ggcloud_variables()

Plots MCA cloud of variables with ggplot2

ggadd_supind()

Adds supplementary individuals to a cloud of individuals

ggadd_supvar()

Adds a categorical supplementary variable to a cloud of variables

ggadd_ellipses()

Adds confidence ellipses to a cloud of individuals

ggadd_kellipses()

Adds k-inertia ellipses to a cloud of individuals

ggadd_chulls()

Adds convex hulls to a cloud of individuals

ggadd_density()

Adds a density layer to the cloud of individuals for a category of a supplementary variable

ggadd_corr()

Adds a heatmap of under/over-representation of a supplementary variable to a cloud of individuals

ggadd_interaction()

Adds the interaction between two categorical supplementary variables to a cloud of variables

ggadd_attractions()

Adds attractions between categories via segments to a cloud of variables

## Descriptive analysis

wtable()

Computes a (possibly weighted) contingency table

phi.table()

Computes the phi coefficient for every cells of a contingency table

pem()

Computes the local and global Percentages of Maximum Deviation from Independence (PEM)

assoc.twocat()

Cross-tabulation and measures of association between two categorical variables

assoc.catcont()

Measures the association between a categorical variable and a continuous variable

assoc.twocont()

Measures the association between two continuous variables

catdesc()

Measures the association between a categorical variable and some continuous and/or categorical variables

condesc()

Measures the association between a continuous variable and some continuous and/or categorical variables

assoc.yx()

Bivariate association measures between a response and predictor variables.

darma()

Describes Associations as in a Regression Model Analysis.

ggassoc_crosstab()

Plots counts and associations of a crosstabulation

ggassoc_phiplot()

Bar plot of phi measures of association of a crosstabulation

ggassoc_boxplot()

Boxplots with violins

ggassoc_scatter()

Scatter plot with a smoothing line

## Miscellaneous

burt()

Computes a Burt table

dichotom()

Dichotomizes the variables in a data frame

flip.mca()

Flips the coordinates of a MCA

angles.csa()

Cosine similarities and angles between CSA and MCA

getindexcat()

Returns the names of the categories in a data frame

medoids()

Computes the medoids of clusters

translate.logit()

Translates logit regression coefficients into percentages

## Datasets

Music

Music (data)

Taste

Taste (data)

Movies

Movies (data)