Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.

Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.

Fit and analysis of finite Mixtures of Mallows models with Spearman Distance for full and partial rankings with arbitrary missing positions. Inference is conducted within the maximum likelihood framework via Expectation-Maximization algorithms. Estimation uncertainty is tackled via diverse versions of bootstrapping as well as via Hessian-based standard errors calculations. The most relevant reference of the methods is Crispino, Mollica, Astuti and Tardella (2023) <doi:10.1007/s11222-023-10266-8>.

Model evaluation based on a modified version of the recursive feature elimination algorithm. This package is designed to determine the optimal model(s) by leveraging all available features.

Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".

Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').