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plsda and splsda functions are implemented to perform PLS Discriminant Analysis (PLS-DA) and sparse PLS-DA respectivelybreast.tumors data set is introduced to illustrate the (s)PLS-DA- PCA can also been performed with missing values using the NIPALS algorithm and 3D plots are also available for PCA
- Network (updated) to display relevant associations between variables for (r)CCA and (s)PLS, with a new similarity function
- A new similarity measure has been included in
cim function and the arguments hclusfuncand distfunc to display Clustered image maps (heatmaps)
- 3D representation to display samples and variables for (r)CCA
- 3D representation to display samples and variables for (s)PLS
- The argument
scaleY has been added to the pls and spls functions
- (s)PLS can also be applied when there is only 1 predictor variable
