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

Given the cost of the software, it is dissapointing to see the XLSTAT-Forecast module does not include BASIC forecast evaluation statistics such as Diebold Mariano, Peseran-Timmerman, Correct Sign, RMSE, MSE, ME, MAE, MAPE etc - the first three st...

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.

To predict the Overall Liking resulted from decreasing the percentages of Too Weak or Too Strong. This method would help clients in prioritizing sensory aspects to improve.

It is possible to enter main effects and k-way interactions into a model now! Often it would be useful to also allow for quadratic and cubic terms .

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/

The scatterplot matrix is a great way to quickly visualize correlations among multivariate data. It is especially helpful if confidence ellipses can be added and if the axes can be easily edited.

The Tanimoto coefficient is the most widely used similarity metric in cheminformatics.

Spatial Autocorrelation Indices (e.g Moran's I) are mainstream of spatial statistics that are worth being implemented in XLSTAT software. They are useful for ecological studies.

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