Package: malp 1.1-1

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_1.1-1.tar.gz
malp_1.1-1.zip(r-4.7-any)malp_1.1-1.zip(r-4.6-any)malp_1.1-1.zip(r-4.5-any)
malp_1.1-1.tgz(r-4.6-any)malp_1.1-1.tgz(r-4.5-any)
malp_1.1-1.tar.gz(r-4.7-any)malp_1.1-1.tar.gz(r-4.6-any)
malp_1.1-1.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:

2.30 score 170 downloads 7 exports 4 dependencies

Last updated from:52c54c1e2b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK113
source / vignettesOK145
linux-releaseOK108
macos-releaseOK86
macos-oldrelOK81
windows-develOK61
windows-releaseOK56
windows-oldrelOK88
wasm-releaseOK118

Exports:ccckFoldMalpmalpplot.malppredict.malpsummary.malpvcov.malp

Dependencies:bootlatticesandwichzoo