Package: ModLR 0.1.29
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>.
Authors:
ModLR_0.1.29.tar.gz
ModLR_0.1.29.zip(r-4.7-any)ModLR_0.1.29.zip(r-4.6-any)ModLR_0.1.29.zip(r-4.5-any)
ModLR_0.1.29.tgz(r-4.6-any)ModLR_0.1.29.tgz(r-4.5-any)
ModLR_0.1.29.tar.gz(r-4.7-any)ModLR_0.1.29.tar.gz(r-4.6-any)
ModLR_0.1.29.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ModLR/json (API)
| # Install 'ModLR' in R: |
| install.packages('ModLR', repos = c('https://ahdar1.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ahdar1/modlr/issues
Last updated from:25c5eb532c. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 122 | ||
| source / vignettes | OK | 200 | ||
| linux-release | OK | 127 | ||
| macos-release | OK | 143 | ||
| macos-oldrel | OK | 180 | ||
| windows-devel | OK | 92 | ||
| windows-release | OK | 98 | ||
| windows-oldrel | OK | 84 | ||
| wasm-release | OK | 92 |
Exports:compare_modelsjohnson_neymanmoderated_regressionplot_moderationsimple_slopes
Dependencies:backportsbroomclicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclelmtestmagrittrpillarpkgconfigpurrrR6RColorBrewerrlangS7sandwichscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compare Moderation Models | compare_models |
| Johnson-Neyman Analysis | johnson_neyman |
| Moderated Regression Model | moderated_regression |
| Plot Moderation Effect | plot_moderation |
| Simple Slopes Analysis | simple_slopes |
