Model-Based Effect Sizes for Multilevel Models R package MLMES version 0.1.2 Computes model-based effect sizes for fixed-effect coefficients in multilevel (hierarchical) models. The coefficient effect sizes are standardized mean differences from zero (d) and unique variance-explained measures (squared semi-partial correlations, sr2). The package also reports variance components and level-specific and total R-squared values. It supports 2-level and 3-level linear and binary logistic models fitted with 'lme4' (Bates et al., 2015) <doi:10.18637/jss.v067.i01>, and 2-level Gaussian and Bernoulli models fitted with 'brms' (Bürkner, 2017) <doi:10.18637/jss.v080.i01>. Sanders, Konold, and Cheng (in press), "Model-based effect sizes for multilevel linear regression coefficients," Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, describe the 2-level linear-model methods. Software Depends: R (>= 3.5.0) Imports: lme4 (>= 1.1.34), Matrix (>= 1.6-5), dplyr, stats Suggests: brms, bayestestR, lmerTest (>= 3.1-0), testthat (>= 3.0.0) Yijun Cheng <chengxb@uw.edu> Comprehensive R Archive Network (CRAN) Yijun Cheng [aut, cre], Elizabeth A. Sanders [aut], Timothy R. Konold [aut], Zhigang Zhang [aut], Zixie Zheng [aut] GPL-3 2026-08-20 application/tgz https://CRAN.R-project.org/package=MLMES doi:10.32614/CRAN.package.MLMES