It would be very useful to include the products in the "Principal coordinates analysis plot". The result would be a plot that is a sort of combination of the "symmetric plot" and the "Principal coordinates analysis plot".

Introduction of kernel functions in PLS can significantly improve the explanatory power of regression. Hope it comes true in future.

see above

MARS is very good for modeling where the variables have a change in importance across a data set. fx=f(x) if a> some # and 0 otherwisesee https://en.wikipedia.org/wiki/Kriging#:~:text=From%20Wikipedia%2C%20the%20free%20encyclopedia,process%20g...

This machine learning technique is a new form of PCA that allows for determination of the set of variables that best explain two groups.https://arxiv.org/abs/1709.06716

We want to add an intensity dimensions to our attributes on a CATA question... for the moment, only CATA analyse are available on XLSTAT.The idea will be to create a CATA questionnaire, and for each attributes quoted, a JAR scale will be presented...

Since there is already a feature for doing PCAs, MFAs, etc. considering both active & supplementary data, it would be very useful adding the same feature for clustering methods as we usually use them paired up.

in the base data, the rows are divided into blocks with colour-coded labels showing their origins. This information is needed in the AHC dendrogram so that the distribution of the blocks can be easily seen without reading the (many and tiny) row l...

Nonlinear regression ids in common use