Package: mlmoderator 0.2.1

mlmoderator: Probing, Plotting, and Interpreting Multilevel Interaction Effects

Provides a unified workflow for probing, plotting, and assessing the robustness of cross-level interaction effects in two-level mixed-effects models fitted with 'lme4' (Bates et al., 2015) <doi:10.18637/jss.v067.i01>. Implements simple slopes analysis following Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals following Johnson and Fay (1950) <doi:10.1007/BF02288864> and Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5>, and grand- or group-mean centering as described in Enders and Tofighi (2007) <doi:10.1037/1082-989X.12.2.121>. Includes a slope variance decomposition that separates fixed-effect uncertainty from random-slope variance (tau11), a contour surface plot of predicted outcomes over the full predictor-by-moderator space, and robustness diagnostics comprising intraclass correlation coefficient shift analysis and leave-one-cluster-out (LOCO) stability checks. Designed for researchers in education, psychology, biostatistics, epidemiology, organizational science, and other fields where outcomes are clustered within higher-level units.

Authors:Subir Hait [aut, cre]

mlmoderator_0.2.1.tar.gz
mlmoderator_0.2.1.zip(r-4.7)mlmoderator_0.2.1.zip(r-4.6)mlmoderator_0.2.1.zip(r-4.5)
mlmoderator_0.2.1.tgz(r-4.6-any)mlmoderator_0.2.1.tgz(r-4.5-any)
mlmoderator_0.2.1.tar.gz(r-4.7-any)mlmoderator_0.2.1.tar.gz(r-4.6-any)
mlmoderator_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
mlmoderator/json (API)

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

Bug tracker:https://github.com/causalfragility-lab/mlmoderator/issues

Datasets:

On CRAN:

Conda:

4.48 score 462 downloads 8 exports 30 dependencies

Last updated from:d082a04529. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK248
source / vignettesOK208
linux-release-x86_64OK178
macos-release-arm64OK103
macos-oldrel-arm64OK157
windows-develOK134
windows-releaseOK121
windows-oldrelOK133
wasm-releaseOK138

Exports:mlm_centermlm_jnmlm_plotmlm_probemlm_sensitivitymlm_summarymlm_surfacemlm_variance_decomp

Dependencies:bootclicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclelme4MASSMatrixminqanlmenloptrR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangS7scalesvctrsviridisLitewithr

Cross-Level Interaction Workflow
What is a cross-level interaction? | Setup | Centering strategy for cross-level interactions | Fit the random-slopes model | Probe simple slopes | Johnson–Neyman region | Visualise | Full summary for reporting | Reporting guidance | Assumptions to check

Last update: 2026-03-19
Started: 2026-03-19

Getting Started with mlmoderator
Overview | The built-in dataset | Step 1: Center variables | Step 2: Fit the model | Step 3: Probe simple slopes | Step 4: Johnson–Neyman interval | Step 5: Plot the interaction | Step 6: Full summary report | Tips

Last update: 2026-03-19
Started: 2026-03-19