Package: malp 1.2

malp: Maximum Agreement Linear Prediction

Provides tools for estimation and prediction using Maximum Agreement Linear Predictors (MALPs). MALPs provide an alternative to least squares linear predictors when agreement between predicted and observed values, as measured by Lin's Concordance Correlation Coefficient (CCC), is of primary interest. Applications include missing value imputation and calibration studies. The package includes functions for model estimation, prediction, statistical inference, cross-validation, and model diagnostics. The implemented methodology is described in Kim et al. (2026) <doi:10.1214/26-EJS2550>.

Authors:Pierre Chausse [aut, cre], Taeho Kim [aut], Edsel A. Pena [aut], George Luta [ctb]

malp_1.2.tar.gz
malp_1.2.zip(r-4.7-any)malp_1.2.zip(r-4.6-any)malp_1.2.zip(r-4.5-any)
malp_1.2.tgz(r-4.6-any)malp_1.2.tgz(r-4.5-any)
malp_1.2.tar.gz(r-4.7-any)malp_1.2.tar.gz(r-4.6-any)
malp_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
malp/json (API)

# Install 'malp' in R:
install.packages('malp', repos = c('https://pchausse.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://codeberg.org/pchausse/malp

Datasets:
  • bodyFat - Data on body fat
  • eye - Data on Optical Coherence Tomography

On CRAN:

Conda:

2.48 score 1 scripts 206 downloads 10 exports 13 dependencies

Last updated from:9d25e49500. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK145
source / vignettesOK218
linux-releaseOK150
macos-releaseOK134
macos-oldrelOK96
windows-develOK65
windows-releaseOK68
windows-oldrelOK78
wasm-releaseOK112

Exports:cccimputekFoldMalpmalpplot.imputeplot.malppredict.malpsummary.imputesummary.malpvcov.malp

Dependencies:askpassbootcurlhttrjsonlitelatticemimeopensslR6sandwichsystexregzoo