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hlmLab

Hierarchical Linear Modeling with Visualization and Decomposition

v0.1.0 · Apr 16, 2026 · MIT + file LICENSE

Description

Provides functions for visualization and decomposition in hierarchical linear models (HLM) for applications in education, psychology, and the social sciences. Includes variance decomposition for two-level and three-level data structures following Snijders and Bosker (2012, ISBN:9781849202015), intraclass correlation (ICC) estimation and design effect computation as described in Shrout and Fleiss (1979) <doi:10.1037/0033-2909.86.2.420>, and contextual effect decomposition via the Mundlak (1978) <doi:10.2307/1913646> specification distinguishing within- and between-cluster components. Supports visualization of random slopes and cross-level interactions following Hofmann and Gavin (1998) <doi:10.1177/014920639802400504> and Hamaker and Muthen (2020) <doi:10.1037/met0000239>. Multilevel models are estimated using 'lme4' (Bates et al., 2015 <doi:10.18637/jss.v067.i01>). An optional 'Shiny' application enables interactive exploration of model components and parameter variation. The implementation follows the multilevel modeling framework of Raudenbush and Bryk (2002, ISBN:9780761919049).

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Check History

OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
WARNING 12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE Jun 8, 2026
WARNING r-devel-linux-x86_64-debian-gcc

for code/documentation mismatches

Fatal error: cannot create 'R_TempDir'
OK 3 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Apr 17, 2026

Dependency Network

Dependencies Reverse dependencies dplyr ggplot2 lme4 scales hlmLab

Version History

1 tracked
new 0.1.0 Apr 16, 2026