MultiLevelOptimalBayes

MultiLevelOptimalBayes (MLOB)

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.

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Features

• Regularized Bayesian estimator optimized for minimum mean squared error (MSE)
• Robust to small sample sizes and low intraclass correlation (ICC)
• Supports covariate adjustment and group-level balancing
• Optional jackknife resampling for improved CI coverage
• Returns full inferential output: point estimates, standard errors, p-values, and confidence intervals
• Clean and user-friendly interface via the mlob() function

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Installation

To install the development version from GitHub:

install.packages("devtools")
devtools::install_github("MLOB-dev/MLOB")

MLOB is available on CRAN under the GPL-3 license. To install the released version:

install.packages("MultiLevelOptimalBayes")

View the Vignette

After installing the package, run the following to open the introductory vignette:

vignette("MultiLevelOptimalBayes-Intro")

Examples

library(MultiLevelOptimalBayes)

Fit a model on the iris dataset

result <- mlob(Sepal.Length ~ Sepal.Width + Petal.Length, data = iris,
group = "Species", conf.level = 0.95)

View results

summary(result)

Limitations

-The estimator assumes approximately equal group sizes. Although balancing helps, unequal sizes may still bias results.

• Grid-search is local 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.

Contributing & Support

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.

Authors

Valerii Dashuk

Binayak Timilsina

Martin Hecht

Steffen Zitzmann

Citation

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

Contact:

[email protected]

[email protected]

[email protected]