Package: msma 3.1

Atsushi Kawaguchi

msma: Multiblock Sparse Multivariable Analysis

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.

Authors:Atsushi Kawaguchi

msma_3.1.tar.gz
msma_3.1.tar.gz(r-4.5-noble)msma_3.1.tar.gz(r-4.4-noble)
msma_3.1.tgz(r-4.4-emscripten)msma_3.1.tgz(r-4.3-emscripten)
msma.pdf |msma.html
msma/json (API)

# Install 'msma' in R:
install.packages('msma', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.48 score 1 packages 11 scripts 194 downloads 8 exports 0 dependencies

Last updated 10 months agofrom:2c2de7a3e4. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 11 2024
R-4.5-linuxOKDec 11 2024

Exports:cvmsmahcmsmamsmancompsearchoptparasearchregparasearchsimdatastrsimdata

Dependencies:

Example for msma

Rendered frommsma.Rmdusingknitr::rmarkdownon Dec 11 2024.

Last update: 2023-08-25
Started: 2018-05-04

Readme and manuals

Help Manual

Help pageTopics
Multiblock Sparse Matrix Analysis Packagemsma-package
Cross-Validationcvmsma
Hierarchical cluster analysishcmsma
Multiblock Sparse Partial Least Squaresmsma msma.default print.msma
Search for Number of Componentsncompsearch plot.ncompsearch print.ncompsearch
Parameters Searchoptparasearch print.optparasearch
Plot msmaplot.msma
Predictionpredict.msma
Regularized Parameters Searchprint.regparasearch regparasearch
Simulate Data setssimdata
Structured Simulate Data setsstrsimdata
Summarizing Fitsprint.summary.msma summary.msma