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missPLS  

Methods and Reproducible Workflows for Partial Least Squares with Missing Data
View on CRAN: Click here


Download and install missPLS package within the R console
Install from CRAN:
install.packages("missPLS")

Install from Github:
library("remotes")
install_github("cran/missPLS")

Install by package version:
library("remotes")
install_version("missPLS", "0.2.1")



Attach the package and use:
library("missPLS")
Maintained by
Frederic Bertrand
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-13
Latest Update: 2026-04-13
Description:
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.
How to cite:
Frederic Bertrand (2026). missPLS: Methods and Reproducible Workflows for Partial Least Squares with Missing Data. R package version 0.2.1, https://cran.r-project.org/web/packages/missPLS. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:30), 0.2.0 (2026-04-13 17:00)
Other packages that cited missPLS R package
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Complete documentation for missPLS
Functions, R codes and Examples using the missPLS R package
Full missPLS package functions and examples
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