missPLS-package: missPLS: Methods and Reproducible Workflows for Partial Least...

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

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

Description

Methods-first tooling for reproducing and extending the partial least squares regression studies on incomplete data described in Nengsih et al. (2019) \Sexpr[results=rd]{tools:::Rd_expr_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.

Author(s)

Maintainer: Frederic Bertrand [e-mail]

Authors:

• Titin Agustin Nengsih

• Myriam Maumy-Bertrand

See Also

Useful links:

• https://fbertran.github.io/missPLS/

• https://github.com/fbertran/missPLS

• Report bugs at https://github.com/fbertran/missPLS/issues

missPLS documentation built on April 30, 2026, 9:09 a.m.