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missPLS: Methods and Reproducible Workflows for Partial Least Squares with Missing Data

Methods-first tooling for reproducing and extending the partial least squares regression studies on incomplete data described in Nengsih et al. (2019) <doi:10.1515/sagmb-2018-0059>. The package provides simulation helpers, missingness generators, imputation wrappers, component-selection utilities, real-data diagnostics, and reproducible study orchestration for Nonlinear Iterative Partial Least Squares (NIPALS)-Partial Least Squares (PLS) workflows.

Version: 0.2.0
Depends: R (≥ 4.1.0)
Imports: mice, plsRglm, stats, utils, VIM
Suggests: bcv, knitr, mlbench, plsdof, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-04-13
DOI: 10.32614/CRAN.package.missPLS (may not be active yet)
Author: Titin Agustin Nengsih [aut], Frederic Bertrand [aut, cre], Myriam Maumy-Bertrand [aut]
Maintainer: Frederic Bertrand <frederic.bertrand at lecnam.net>
BugReports: https://github.com/fbertran/missPLS/issues
License: GPL-3
URL: https://fbertran.github.io/missPLS/, https://github.com/fbertran/missPLS
NeedsCompilation: no
Citation: missPLS citation info
Materials: NEWS
CRAN checks: missPLS results

Documentation:

Reference manual: missPLS.html , missPLS.pdf
Vignettes: missPLS (source, R code)

Downloads:

Package source: missPLS_0.2.0.tar.gz
Windows binaries: r-devel: not available, r-release: missPLS_0.2.0.zip, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): missPLS_0.2.0.tgz, r-oldrel (x86_64): missPLS_0.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=missPLS to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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