Would be nice to be able to integrate the area under the curve in linear and non-linear models.

I think XLSTAT provides partial least squares models but not general least squares. This would be very practical for time series analysis, for instance.

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

In a linear hierarchy regression, it is important to know the change in R-squared to know if additional variables in a model increase the association with the predictor variables between the models (independent variables).

Hello XLSTAT team. Any plans to add the eta squared and partial eta squared for ANOVA and regression ? These measures are commonly used to study the effect size. Plus, it is easy to implement and straightforward to interpret !

Here is a series of references for checking for groups of outliers in the data: https://lemakistatheux.wordpress.com/category/tests-statistique-indices-de-liaison-et-coefficients-de-correlation/les-tests-pour-la-detection-doutliers/

Robust PCA is as the name implies a more robust version of PCA. Extremely useful for large but sparse datasets (many biological areas e.g. ecology, metabolomics, transcriptomics; and computer science e.g. visual recognition).Provides a PCA which i...

When performing the Kruskal-Wallis analysis of variance on k samples, up until now, you can choose between Dunn, Conover-Iman and Steel-Dwass-Critchlow-Fligner with the addition of Bonferroni correction (for the first two mentioned, respectively) ...

Rolling multiple regression is extremely useful in that it enables you to see how stable the beta coefficients are over time. This is a very common issue that comes up regularly when you deal with financial time series.

This would be of general interest to a lot of people. It allows to understand how robust is your model if data is removed. See examples here: https://en.wikipedia.org/wiki/Sensitivity_analysis