Software Development
AlterLab Multilevel Models
Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using statsmodels MixedLM and bambi (Bayesian on PyMC) in Python, or the field-standard R lme4 / glmmTMB / brms via Rscript. It enforces the reporting items reviews find under-reported: full fixed + random specification, centering, variance components + ICC, estimation method, assumption checks, model comparisons, and effect sizes. Use when data are grouped/nested (students in schools, repeated measures, panel/longitudinal) and the question concerns within- vs between-cluster variation. For general single-level regression prefer alterlab-statsmodels; for panel fixed effects used for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite.
- Publisher / source
- AlterLab-IEU/AlterLab-Academic-Skills (found on the CSV list)
- Upstream record
- AlterLab-IEU/AlterLab-Academic-Skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
a44a3e7581567503603771f84d9035e55721e5687e1f985b9af98e911f57ec1b