Overview | Simulated data | PCA | Sparse PCA | Supervised sparse PCA | PLS | Prediction | Session information
Package: msma 4.0
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. Version 4.0 adds opt-in supervised sparse soft-structured principal component analysis, reconstruction, and repeated split reconstruction-based parameter selection while preserving the default Version 3.2 computational paths.
Authors:
msma_4.0.tar.gz
msma_4.0.zip(r-4.7-any)msma_4.0.zip(r-4.6-any)
msma_4.0.tar.gz(r-4.7-any)msma_4.0.tar.gz(r-4.6-any)
msma_4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
msma/json (API)
| # Install 'msma' in R: |
| install.packages('msma', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:23616c1044. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 121 | ||
| source / vignettes | OK | 153 | ||
| linux-release | OK | 114 | ||
| windows-devel | OK | 81 | ||
| windows-release | OK | 78 | ||
| wasm-release | OK | 90 |
Exports:cvmsmahcmsmamsmancompsearchoptparasearchregparasearchs4pca_conformal_selects4pca_reconstructsimdatastrsimdata
Dependencies:
Getting Started with msma
Last update: 2026-10-03
Started: 2026-08-26
Model and Parameter Selection in msma
Overview | Selecting the number of components | Selecting regularization parameters | Combined selection | PLS selection | Cross-validation | Version 3.2 super-level methods | Computational recommendations | Session information
Last update: 2026-10-03
Started: 2026-08-26
Multiblock and Nested Component Analysis
Multiblock data | Multiblock PCA | Sparse multiblock PCA | Nested components | Supervised multiblock PCA | Multiblock PLS | Session information
Last update: 2026-10-03
Started: 2026-08-26
NMF-Based Super-Level Analysis and ECMSMA
Overview | Super-level methods | Non-negative transformations | Non-negativity and reproducibility | Clustering solutions | Multiple supervision variables | One-column matrix compatibility | Practical recommendations | Session information
Last update: 2026-10-03
Started: 2026-08-26
Supervised Sparse Soft-Structured PCA with msma
Overview | Repeated split conformal model selection
Last update: 2026-10-03
Started: 2026-10-03
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Cross-Validation | cvmsma |
| Hierarchical cluster analysis | hcmsma |
| Multiblock Sparse Partial Least Squares | msma msma.default print.msma |
| Search for Number of Components | ncompsearch plot.ncompsearch print.ncompsearch |
| Parameters Search | optparasearch print.optparasearch |
| Plot msma | plot.msma |
| Prediction | predict.msma |
| Regularized Parameters Search | print.regparasearch regparasearch |
| Repeated split conformal selection for S4PCA | s4pca_conformal_select |
| Reconstruct data from an S4PCA fit | s4pca_reconstruct |
| Simulate Data sets | simdata |
| Structured Simulate Data sets | strsimdata |
| Summarizing Fits | print.summary.msma summary.msma |