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.

✨ 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

📦 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("MLOB")

📦 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 (±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.

📦 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:

martin.hecht@hsu-hh.de

steffen.zitzmann@medicalschool-hamburg.de

multilob@outlook.com

mirror server hosted at Truenetwork, Russian Federation.