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).