Data is becoming ever more both high dimensional and sparse. Kernel PCA can enable a reliable and relatively simple dimensionality reduction. Kernels have already been introduced in Xlstat via SVMs.

Taylor diagram is available only on MATLAB, R and Python.It provides a visual framework for comparing a suite of variables from one or more test data sets to one or more reference data sets.The Taylor Diagram is a very useful model evaluation tool.

More info regarding conditional probabilities

Hi, I have a question regarding the modeling data section. I have an XLSTAT-Premium version to deal with the metabolomics data. PCA and PLS-DA contributed a lot to explore my dataset. However, nowadays Orthogonal Partial least squares discriminant...

affordable and simple option for making word clouds for instance...

add some plot to show the panel's repeatability , consistency

So that people won't need another software

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...