## ========
Version 1.5
New functions:
- assoc.twocat(): measures the association between two categorical variables
- assoc.catcont(): measures the association between a categorical variable and a continuous variable
- 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}
- ggcloud_indiv(): cloud of individuals using ggplot
- ggcloud_variables(): cloud of variables using ggplot
- ggadd_supvar(): adds a supplementary variable to a cloud of variables using ggplot
- ggadd_interaction(): adds the interaction between two variables to a cloud of variables using ggplot
- ggadd_ellipses(): adds concentration ellipses to a cloud of individuals using ggplot
Changes in existing functions:
- conc.ellipses(): additional options
## ========
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
## ========
Version 1.3
Changes in existing functions:
- dimdesc.MCA(): now uses weights
Bug fixes:
- dimdesc.MCA(): problem of compatibility next to a FactoMineR update
## ========
Version 1.2
New functions:
- dimvtest(): computes test-values for supplementary variables
Changes in existing functions:
- dimeta2(): now allows 'stMCA' objects
## ========
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).