Peer-reviewedCC BYSoftwareX
MBAnalysis: An R package for multiblock data analysis
Abstract
Multiblock data, also known as multiview data, is increasingly encountered in various fields, such as health sciences, environmental chemistry, food quality and control. It refers to situations in which several blocks of variables share the same set of observations. The R package MBAnalysis is dedicated to the analysis of multiblock data for exploratory data reduction and prediction purposes. It is the first package to unify and combine unsupervised and supervised multiblock factorial methods based on latent components. These methods are implemented alongside decision aids, validation tools and companion graphical functions. All these methods apply to sets of quantitative variables with no missing values.
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BibTeX
@article{mahieu2026mbanalysis,
title = {{MBAnalysis: An R package for multiblock data analysis}},
author = {Benjamin Mahieu and E. Tchandao Mangamana and E. Vigneau and V. Cariou},
journal = {SoftwareX},
year = {2026},
month = sep,
doi = {10.1016/j.softx.2026.102780},
url = {https://hal.science/hal-05738961v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - MBAnalysis: An R package for multiblock data analysis
AU - Benjamin Mahieu
AU - E. Tchandao Mangamana
AU - E. Vigneau
AU - V. Cariou
PY - 2026
DA - 2026/09/04
JO - SoftwareX
DO - 10.1016/j.softx.2026.102780
UR - https://hal.science/hal-05738961v1
AB - Multiblock data, also known as multiview data, is increasingly encountered in various fields, such as health sciences, environmental chemistry, food quality and control. It refers to situations in which several blocks of variables share the same set of observations. The R package MBAnalysis is dedicated to the analysis of multiblock data for exploratory data reduction and prediction purposes. It is the first package to unify and combine unsupervised and supervised multiblock factorial methods based on latent components. These methods are implemented alongside decision aids, validation tools and companion graphical functions. All these methods apply to sets of quantitative variables with no missing values.
SN - 2352-7110
ER -