Methods and Reproducible Workflows for Partial Least Squares with Missing Data R package missPLS version 0.2.0 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. Software Depends: R (>= 4.1.0) Imports: mice, plsRglm, stats, utils, VIM Suggests: bcv, knitr, mlbench, plsdof, rmarkdown, testthat (>= 3.0.0) Frederic Bertrand <frederic.bertrand@lecnam.net> Comprehensive R Archive Network (CRAN) Titin Agustin Nengsih [aut], Frederic Bertrand [aut, cre], Myriam Maumy-Bertrand [aut] GPL-3 2026-04-13 application/tgz https://CRAN.R-project.org/package=missPLS doi:10.32614/CRAN.package.missPLS