a gateway to R constructs would open up the capability of xlstat

Sensory and food industry.

While PCA is used to explore the relation between several quantitative variables, MCA (Multiple Correspondance Analysis) is used to explore the relation between several categorical variables. Some methods (PCAmix method for exemple) can deal with ...

Currently the colors for groups are automatically chosen... if we could pick the colors ourselves it would be easier to track the same groups when you are doing lots of scatter plots

The quality of an ANCOVA depends on the heteroscedasticity and the normality of residuals. Currently, these two aspects are not part of the output of the ANCOVA analysis. They have to be manually calculated (in the basic version for normality, i...

Is it very useful to have the possibility to apply MFA not only to continuous data (submitted to PCA) but also to frequency tables (e.g. CATA results, that should be submitted to CA). At the moment you are forced to use R to do that.

Random Forests is a mainstream machine learning algorithm that is worth being implemented in XLSTAT. Maybe the XLSTAT decision trees code could be used as a baseline to develop Random Forests?

We generally do not know the number of classes in advance. Thus, we need to test for goodness of fit or we may just be looking at statistical noise. Please consider adding the cophenetic correlation coefficient (Sokal and Rohlf, 1962 or Farris) or...

Current XLSTAT contains only Electre method of multicriteria analysis. It would be of great benefit to users to have AHP, Cheng's Extent, Promethee I & II, Van Lohrvan method, Cheng's entropy and similar methods of decision making.