Multiblock Data Fusion in Statistics and Machine Learning

Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("multiblock")

0.8.0 by Kristian Hovde Liland, a month ago


https://khliland.github.io/multiblock/, https://github.com/khliland/multiblock/


Report a bug at https://github.com/khliland/multiblock/issues/


Browse source code at https://github.com/cran/multiblock


Authors: Kristian Hovde Liland [aut, cre] , Solve Sæbø [ctb] , Stefan Schrunner [rev]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports ade4, car, FactoMineR, geigen, knitr, lme4, MASS, MFAg, mixlm, plotrix, pls, plsVarSel, pracma, progress, r.jive, Rcpp, RegularizedSCA, RGCCA, RSpectra, SSBtools

Suggests rmarkdown

Linking to Rcpp, RcppEigen


See at CRAN