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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "hlmLab" in publications use:'
type: software
license: MIT
title: 'hlmLab: Hierarchical Linear Modeling with Visualization and Decomposition'
version: 0.1.0
doi: 10.32614/CRAN.package.hlmLab
abstract: 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) <https://doi.org/10.1037/0033-2909.86.2.420>,
  and contextual effect decomposition via the Mundlak (1978) <https://doi.org/10.2307/1913646>
  specification distinguishing within- and between-cluster components. Supports visualization
  of random slopes and cross-level interactions following Hofmann and Gavin (1998)
  <https://doi.org/10.1177/014920639802400504> and Hamaker and Muthen (2020) <https://doi.org/10.1037/met0000239>.
  Multilevel models are estimated using 'lme4' (Bates et al., 2015 <https://doi.org/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).
authors:
- family-names: Hait
  given-names: Subir
  email: haitsubi@msu.edu
  orcid: https://orcid.org/0009-0004-9871-9677
repository: https://causalfragility-lab.r-universe.dev
repository-code: https://github.com/causalfragility-lab/hlmLab
commit: 039879e11549fa962880280fe7d9081f41901ad0
url: https://github.com/causalfragility-lab/hlmLab
date-released: '2026-04-21'
contact:
- family-names: Hait
  given-names: Subir
  email: haitsubi@msu.edu
  orcid: https://orcid.org/0009-0004-9871-9677