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) ). It also provides functions
for the translation of logit models coefficients into percentages (Deauvieau (2010) ), weighted contingency tables, an association
measure for contingency tables ("Percentages of Maximum Deviation from Independence", aka PEM, see Cibois (1993) ) and some tools to measure bivariate associations between variables
(phi, Cramr V, correlation coefficient, eta-squared...).

News

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Version 1.4

New functions:

translate.logit(): translates logit models coefficients into percentages

tabcontrib(): displays the categories contributing most to MCA dimensions

Changes in existing functions:

varsup(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloud

textvarsup(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloud

conc.ellipse(): with csMCA, the length of variable argument can be equal to the size of the cloud or the subcloud

plot.multiMCA(): 'threshold' argument, aimed at selecting the categories most associated to axes

plot.stMCA(): 'threshold' argument, aimed at selecting the categories most associated to axes

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Version 1.3

Changes in existing functions:

dimdesc.MCA(): now uses weights

Bug fixes:

dimdesc.MCA(): problem of compatibility next to a FactoMineR update

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Version 1.2

New functions:

dimvtest(): computes test-values for supplementary variables

Changes in existing functions:

dimeta2(): now allows 'stMCA' objects

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Version 1.1

New functions:

wtable(): works as table() but allows weights and shows NAs as default

prop.wtable(): works as prop.table() but allows weights and shows NAs as default

Changes in existing functions:

multiMCA(): RV computation is now an option, with FALSE as default,
which makes the function execute faster

Bug fixes:

textvarsup(): there was an error with the supplementary
variable labels when resmca was of class "csMCA".

Error fixes:

textvarsup(): plots supplementary variables on the cloud of categories (and not
the cloud of individuals as it was mentioned in help).