MPCA is a complete suite of tools to make discrete principal components analysis on datasets of size 100 MB or more. AAM is a comprehensive suite of tools to make discrete principal components analysis on data sets of size 100 MB or more. Scaling is done using sparse vectors, multi-threading mapping memory, and other tricks.Reports POSIX, file dumping utilities and other utilities are included. The general problem of discrete components analysis is variously called grade of membership, PLSA, non-neg.matrix factorization, multinomial admixtures, LDA, and multinomial PCA.What's new in this version: · Some links with the ALVIS allow system software to be used to create models and annotate about linguistically tagged content. · Few cleanings with linkBags Perl utilities have been moved from CPAN. · To see some of these models in action, visit the Demos research.