I'm a very happy and satisfied customer using XLSTAT. I've recently upgraded to MS Office 365 and was hoping that you could guide me to an App, or the process by which I can continue to use my XLStat with the online of MS Excel.

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

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).

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

In time series analysis, dynamic time warping (DTW) is one of the algorithms for measuring similarity between two temporal sequences, which may vary in speed. The ability to analyze and then cluster times series based on dynamic time warping wou...

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

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.

important to know how many subjects are at risk at each major time interval. I have to look this up in the table and add it manually for each group being analyzed.

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.