The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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).
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=hlmLab
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.
[aut,
cre]