multiblock: 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.

Version: 0.8.0
Depends: R (≥ 3.5.0)
Imports: ade4, car, FactoMineR, geigen, knitr, lme4, MASS, MFAg, mixlm, plotrix, pls, plsVarSel, pracma, progress, r.jive, Rcpp, RegularizedSCA, RGCCA, RSpectra, SSBtools
LinkingTo: Rcpp, RcppEigen
Suggests: rmarkdown
Published: 2021-09-21
Author: Kristian Hovde Liland ORCID iD [aut, cre], Solve Sæbø [ctb], Stefan Schrunner [rev]
Maintainer: Kristian Hovde Liland <kristian.liland at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README
CRAN checks: multiblock results


Reference manual: multiblock.pdf
Vignettes: A. Data handling
B. Basic analysis
C. Unsupervised multiblock analysis
E. Supervised multiblock analysis
F. Complex multiblock analysis


Package source: multiblock_0.8.0.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): multiblock_0.8.0.tgz, r-release (x86_64): multiblock_0.8.0.tgz, r-oldrel: multiblock_0.8.0.tgz


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