- bug fix with in
`medoids()`

`angles.csa()`

: Computes the cosines similarities and angles between the dimensions of a CSA and those of a MCA.

- bug fix with vignettes
- bug fix with NA values in
`dichotom()`

(thanks to @juba) - bug fix with dim option in
`dimdescr()`

`assoc.twocat()`

: PEM are no longer computed.`ggadd_supvar()`

: for shapes, a value of 0 is mapped to a size of 0 and new shapesize option (as suggested by @osturnus)

`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_kellipses()`

: adds concentration ellipses to a cloud of individuals, using ggplot`ggadd_chulls()`

: adds convex hulls to a cloud of individuals, using ggplot`ggassoc_crosstab()`

: plots counts and associations of a crosstabulation, using ggplot`ggassoc_phiplot()`

: bar plot of phi measures of association of a crosstabulation, using ggplot`ggassoc_boxplot()`

: displays of boxplot and combines it with a violin plot, using ggplot`ggassoc_scatter()`

: scatter plot with a smoothing line, using ggplot`dimdescr()`

: works with`condesc()`

instead of`FactoMineR::condes()`

and takes row weights into account.`dimtypicality()`

: computes typicality tests for supplementary variables`ggadd_attractions()`

: adds attractions between categories (via segments) to a cloud of variables`ggadd_supind()`

: adds supplementary individuals to a cloud of individuals, using ggplot`flip.mca()`

: flips the coordinates of the individuals and the categories on one or more dimensions of a MCA

`dimdesc.MCA()`

: replaced by`dimdescr()`

`dimvtest()`

: use`dimtypicality()`

instead

`ggcloud_indiv()`

: the density of points can be represented as an additional layer through contours or hexagon bins`catdesc()`

and`condesc()`

: allow weights`catdesc()`

and`condesc()`

: new nperm and distrib options`catdesc()`

and`condesc()`

: new robust option`assoc.twocont()`

,`assoc.twocat()`

and`assoc.catcont()`

: nperm option is set to NULL by default`darma()`

: nperm is set to 100 by default`ggcloud_variables()`

and`ggcloud_indiv()`

: a few changes in the theme (grids are removed, etc.)`ggcloud_indiv()`

and`ggadd_ellipses()`

: new size option`ggcloud_variables()`

: new min.ctr option to filter categories according to their contribution (for objects of class MCA, speMCA and csMCA)`ggcloud_variables()`

: new max.pval option to filter categories according to the p-value derived from their test-value (for objects of class stMCA and multiMCA)`ggcloud_variables()`

: prop argument can take values “vtest1” and “vtest2”`ggcloud_variables()`

: for shapes and colors, variables are used in their order of appearance in the data instead of alphabetical order`ggcloud_variables()`

: new face argument to use font face to identify the most contributing categories`homog.test()`

: gives the p-values in addition to the test statistics`dimeta2()`

: l argument renamed to vars and n argument removed`varsup()`

: also computes typicality tests and correlation coefficients`conc.ellipse()`

: several kinds of inertia ellipses can be plotted thanks to the kappa option`ggadd_ellipses()`

: level is set to 0.05 by default, which corresponds to conventional confidence ellipses. Option ‘points’ to choose to color the points or not.`modif.rate()`

: computes raw and modified rates`homog.test()`

: new dim argument`modif.rate()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggcloud_variables()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggcloud_indiv()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_supvar()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_interaction()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`dimeta2()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`dimcontrib()`

: compatibility with objects of class MCA, speMCA and csMCA`tabcontrib()`

: compatibility with objects of class MCA, speMCA and csMCA`homog.test()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`varsup()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_chulls()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_corr()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_density()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_ellipses()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA`ggadd_kellipses()`

: compatibility with objects of class MCA, speMCA, csMCA, stMCA and multiMCA

`csMCA()`

,`speMCA()`

and`translate.logit()`

: now work with tibbles`ggcloud_variables()`

: now works when shapes=TRUE and there are many variables`assoc.twocat()`

: bug fix for empty cells`multiMCA()`

: bug fix with eigen values

`phi.table()`

: computes phi coefficient for every cells of a contingency table`assoc.twocont()`

: measures the association between two continuous variables with Pearson, Spearman and Kendall correlations and a permutation test.`assoc.yx()`

: computes bivariate association measures between a response and predictor variables`darma()`

: computes bivariate association measures between a response and predictor variables, displaying results in a form looking like the summary of a regression model analysis.

`assoc.twocat()`

: bug fix with warning`ggcloud_variables()`

: bug fix when`prop`

not NULL.`pem()`

: bug fix with NA values`translate.logit()`

: results for multinomial models were instable

`wtable()`

: can compute percentages (`prop.wtable()`

is removed)`assoc.twocat()`

: Cramer’s V instead of V-squared, permutation p-values, Pearson residuals, percentage of maximum deviation from independence, summary data frame`assoc.twocat()`

: better handling of NAs`assoc.twocat()`

: faster computation`assoc.catcont()`

: permutation p-values`ggcloud_variables()`

: improved color management`pem()`

: one can choose to sort rows and columns or not- weights are allowed in functions
`phi.table()`

,`pem()`

,`assoc.twocat()`

,`assoc.twocont()`

,`assoc.catcont()`

and`assoc.yx()`

`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 confidence ellipses to a cloud of individuals, using ggplot

`conc.ellipses()`

: additional options

`translate.logit()`

: translates logit models coefficients into percentages`tabcontrib()`

: displays the categories contributing most to MCA dimensions

`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

`dimdesc.MCA()`

: now uses weights

`dimdesc.MCA()`

: problem of compatibility next to a FactoMineR update

`dimvtest()`

: computes test-values for supplementary variables

`dimeta2()`

: now allows`stMCA`

objects

`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

`multiMCA()`

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

`textvarsup()`

: there was an error with the supplementary variable labels when`resmca`

was of class`csMCA`

.

`textvarsup()`

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