ModLR: Information-Theoretic Approach for Moderation Analysis

Provides a robust implementation of information-theoretic moderation analysis using multi-model inference based on Akaike's Information Criterion (AIC) and its small-sample corrected form (Corrected AIC). The package enables researchers to compare competing model specifications and helps distinguish true interaction effects from nonlinear relationships that may produce spurious moderation. The methods build on Daryanto (2019) <doi:10.1016/j.jbusres.2019.06.012>.

Version: 0.1.29
Imports: stats, ggplot2, broom, lmtest, sandwich, rlang
Suggests: knitr, rmarkdown
Published: 2026-05-29
DOI: 10.32614/CRAN.package.ModLR (may not be active yet)
Author: Ahmad Daryanto [aut, cre]
Maintainer: Ahmad Daryanto <ahdar_2000 at yahoo.com>
License: MIT + file LICENSE
URL: https://github.com/ahdar1/ModLR
NeedsCompilation: no
Citation: ModLR citation info
Materials: README
CRAN checks: ModLR results

Documentation:

Reference manual: ModLR.html , ModLR.pdf
Vignettes: Introduction to ModLR (source, R code)

Downloads:

Package source: ModLR_0.1.29.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): ModLR_0.1.29.tgz, r-oldrel (arm64): ModLR_0.1.29.tgz, r-release (x86_64): ModLR_0.1.29.tgz, r-oldrel (x86_64): ModLR_0.1.29.tgz

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