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msma  

Multiblock Sparse Multivariable Analysis
View on CRAN: Click here


Download and install msma package within the R console
Install from CRAN:
install.packages("msma")

Install from Github:
library("remotes")
install_github("cran/msma")

Install by package version:
library("remotes")
install_version("msma", "3.1")



Attach the package and use:
library("msma")
Maintained by
Atsushi Kawaguchi
[Scholar Profile | Author Map]
First Published: 2016-01-01
Latest Update: 2024-02-14
Description:
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.
How to cite:
Atsushi Kawaguchi (2016). msma: Multiblock Sparse Multivariable Analysis. R package version 3.1, https://cran.r-project.org/web/packages/msma. Accessed 07 May. 2025.
Previous versions and publish date:
0.7 (2016-01-01 21:47), 1.0 (2018-03-01 13:38), 1.1 (2018-05-04 05:50), 1.2 (2019-06-02 14:10), 2.0 (2019-09-01 15:10), 2.1 (2020-02-06 12:00), 2.2 (2021-06-25 13:50), 3.0 (2023-08-25 06:40)
Other packages that cited msma R package
View msma citation profile
Other R packages that msma depends, imports, suggests or enhances
Complete documentation for msma
Functions, R codes and Examples using the msma R package
Some associated functions: cvmsma . hcmsma . msma-internal . msma-package . msma . ncompsearch . optparasearch . plot.msma . predict.msma . regparasearch . simdata . strsimdata . summary.msma . 
Some associated R codes: Full msma package functions and examples
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