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:
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