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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.
Getting started
Browse package contents
Package details |
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| Author | Titin Agustin Nengsih [aut], Frederic Bertrand [aut, cre], Myriam Maumy-Bertrand [aut] |
| Maintainer | Frederic Bertrand <frederic.bertrand@lecnam.net> |
| License | GPL-3 |
| Version | 0.2.1 |
| URL | https://fbertran.github.io/missPLS/ https://github.com/fbertran/missPLS |
| Package repository | View on CRAN |
| Installation |
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