Package: missPLS 0.2.0

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.

Authors:Titin Agustin Nengsih [aut], Frederic Bertrand [aut, cre], Myriam Maumy-Bertrand [aut]

missPLS_0.2.0.tar.gz
missPLS_0.2.0.zip(r-4.7)missPLS_0.2.0.zip(r-4.6)missPLS_0.2.0.zip(r-4.5)
missPLS_0.2.0.tgz(r-4.6-any)missPLS_0.2.0.tgz(r-4.5-any)
missPLS_0.2.0.tar.gz(r-4.7-any)missPLS_0.2.0.tar.gz(r-4.6-any)
missPLS_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
missPLS/json (API)

# Install 'missPLS' in R:

install.packages('missPLS', repos = c('https://fbertran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fbertran/misspls/issues

Pkgdown/docs site:https://fbertran.github.io

Datasets:

bromhexine - Bromhexine dataset
octane - Octane dataset
ozone_complete - Complete-case ozone dataset
tetracycline - Tetracycline dataset

On CRAN:

4.00 score 1 stars 8 scripts 452 downloads 8 exports 145 dependencies

Last updated from:abea2f35e9. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	210
source / vignettes	OK	296
linux-release-x86_64	OK	171
macos-release-arm64	OK	100
macos-oldrel-arm64	OK	153
windows-devel	OK	108
windows-release	OK	99
windows-oldrel	OK	103
wasm-release	OK	148

Exports:add_missingness diagnose_real_data impute_pls_data run_real_data_study run_simulation_study select_ncomp simulate_pls_data summarize_simulation_study

Dependencies:abind backports bbotk bipartite bit bit64 boot broom car carData checkmate class cli clipr cluster coda codetools colorspace corpcor cowplot cpp11 crayon data.table DEoptimR Deriv digest doBy dotCall64 dplyr e1071 evaluate farver fields forcats foreach forecast Formula fracdiff future future.apply generics ggplot2 glmnet globals glue gtable haven hms igraph isoband iterators jomo jsonlite labeling laeken lattice lgr lifecycle listenv lme4 lmtest magrittr maps MASS Matrix MatrixModels mgcv mice microbenchmark minqa mirai mitml mlbench mlr3 mlr3learners mlr3measures mlr3misc mlr3pipelines mlr3tuning modelr mvtnorm nanonext network nlme nloptr nnet numDeriv ordinal palmerpenguins pan paradox parallelly pbkrtest permute pillar pkgconfig plsRglm prettyunits progress proxy PRROC purrr quantreg R6 ranger rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr reformulas rlang robustbase rpart S7 scales shape sna sp spam SparseM statnet.common stringi stringr survival tibble tidyr tidyselect timeDate tzdb ucminf urca utf8 uuid vcd vctrs vegan VIM viridisLite vroom withr xgboost zoo

missPLS

Simulate a dataset | Add missingness and select components | Impute and re-fit | Real-data diagnostics | Study runners

Rendered frommissPLS.Rmdusingknitr::rmarkdown

Last update: 2026-04-07
Started: 2026-04-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add missing values to a predictor matrix	add_missingness
Bromhexine dataset	bromhexine
Diagnose a real dataset	diagnose_real_data
Impute a predictor matrix	impute_pls_data
Octane dataset	octane
Complete-case ozone dataset	ozone_complete
Run a real-data study	run_real_data_study
Run a simulation study	run_simulation_study
Select the number of PLS components	select_ncomp
Simulate PLS data	simulate_pls_data
Summarize simulation or real-data study results	summarize_simulation_study
Tetracycline dataset	tetracycline