hlmLab: Hierarchical Linear Modeling with Visualization and
Decomposition
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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