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MLOB is an R package for estimating between-group effects in multilevel latent variable models using an optimally regularized Bayesian estimator. It is especially useful for small-sample settings, low ICC data, and hierarchical models commonly used in psychology, education, and social sciences.
mlob()
functionTo install the development version from GitHub:
install.packages("devtools")
::install_github("MLOB-dev/MLOB") devtools
MLOB is available on CRAN under the GPL-3 license. To install the released version:
install.packages("MLOB")
After installing the package, run the following to open the introductory vignette:
vignette("MultiLevelOptimalBayes-Intro")
library(MultiLevelOptimalBayes)
<- mlob(Sepal.Length ~ Sepal.Width + Petal.Length, data = iris,
result group = "Species", conf.level = 0.95)
summary(result)
-The estimator assumes approximately equal group sizes. Although balancing helps, unequal sizes may still bias results.
Grid-search is local (±5σ) around the ML estimate; global optimum is found with high probability but is not guaranteed.
Jackknife resampling improves inference in small samples but can be computationally heavy in larger samples.
Currently supports two-level models with continuous outcomes only. Extensions to GLMMs or 3+ level models are future work.
Please open an issue at:
https://github.com/MLOB-dev/MLOB/issues
Users may also join discussions or suggest enhancements on the Discussions page at
https://github.com/MLOB-dev/MLOB/discussions.
Valerii Dashuk
Binayak Timilsina
Martin Hecht
Steffen Zitzmann
If you use MLOB in your research, please cite:
Dashuk, V., Hecht, M., Luedtke, O., Robitzsch, A., & Zitzmann, S. (2024). An Optimally Regularized Estimator of Multilevel Latent Variable Models, with Improved MSE Performance https://doi.org/10.13140/RG.2.2.18148.39048
steffen.zitzmann@medicalschool-hamburg.de
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.
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