In the procedure Similarity /Dissimilarity matrices but also in Cluster Analysis it is possible to use a variety of similarity and dissimilarity measures. Choosing the right one is most important. To understand all these measures it would be han...

Parallel analysis to indicate how many factors should be retained in the PCA. Useful to anyone running a PCA and/or using a PCA prior to PrefMapping.

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-vali...

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.

Discussed in classics such as Jacques Bertin's 1967 Semiology of graphics and Helmut Spaths Cluster analysis algorithms (which also describes Walter D. Fisher's discretization algorithm implemented in XLStat Fisher procedure) such matrix reorderi...

Allow Likert-type scale data to be used to automatically generate k*k pairwise comparisons, then populate a k x k square by summing the columns, to populate a k x k square matrix, and take the geometric mean of each row, then normalize the geometr...

I was hoping to use your program to reproduce figures like these that were original produced by http://www.biosoft.hacettepe.edu.tr/easyROC/ using data like the .txt files provided. Also need to provide parametric (curved) option. If one already e...