It s great that you have the SVM included but in order to be actually useful for research these are the things that are needed for optimum model selection. Now the models are not optimized. 1. Cross-Validation: you need to add a k-fold cross-val...

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

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 ...

In the {Significance by cell (Fisher's exact test)} table, we can only get < or >.It would be valuable to have the corresponding p-values per cell.

Sensory and food industry.

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?

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.

The Nash-Sutcliffe efficiency , Willmott's index and Model Performance Index (MPI) are suitable indices to add XLSTAT for assessing model performance.

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, in ...