tivariate functional
principal components are retained into the MFPCA model such that
together they explain a fraction of variance greater than
<code>tot_variance_explained</code>,</li>
<li>if “PRESS”, each j-th functional principal component is retained
into the MFPCA model if, by adding it to the set of the first j-1
functional principal components, then the predicted residual error sum
of squares (PRESS) statistic decreases, and at the same time the
fraction of variance explained by that single compo