PCA - Principal Component Analysis, an alternative?

Dear All,


Im working with data science and one of my favorites is PCA - Principal Component Analysis.

Gnumerics is really great in many ways, but the present PCA has some weaknesses.

I found the R library “mixOmics” which has a great PCA (and also PLS).

This supports (among other things) :

·        missing values

·        all matrix forms

·        scaling/centering

·        NIPALS and SVD


Suggestion : Can the present PCA be replaced with the one in R “mixOmics” ?


If this is of interest/possible, I would gladly help with suggestions around implementation etc.


Thanks for a Great software







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