Peter Dunson

Durham, NC

peteradunsonn@gmail.com

I am a junior at Kenyon College studying Mathematics and Computer Science, and an undergraduate researcher working with Dr. Ciprian Crainiceanu at Johns Hopkins Biostatistics.

My research focuses on Bayesian factor models and shrinkage priors. I develop factorverse, an R package for fitting a variety of linear Gaussian Bayesian factor models under different shrinkage and sparsity priors, with Gibbs samplers written in C++. It also provides simulation and diagnostic functions for evaluating fits. I coauthored an accompanying tutorial paper with Dr. Crainiceanu introducing these methods and their implementation.

I am also a varsity swimmer at Kenyon College and a First Team All-American.

news

Jul 13, 2026	Our tutorial paper, Tutorial for Bayesian Factor Models, is now available on arXiv!

selected publications

R pkg
factorverse: Tools for Bayesian Factor Analysis with Rcpp

Peter Dunson

2026

Abs Bib Code

An R package for fitting a variety of linear Gaussian Bayesian factor models under different shrinkage and sparsity priors on the loading matrix, using Gibbs samplers implemented in C++ via RcppArmadillo. All methods share a single interface, so switching priors is a one-word change. The package also provides simulation and diagnostic functions for evaluating fits.
@software{dunson2026factorverse, title = {factorverse: Tools for Bayesian Factor Analysis with Rcpp}, author = {Dunson, Peter}, year = {2026}, version = {0.1.0}, url = {https://github.com/peterdunson/factorverse}, }
arXiv
Tutorial for Bayesian Factor Models

Peter Dunson and Ciprian M. Crainiceanu

arXiv preprint arXiv:2607.11819, 2026

Abs arXiv Bib HTML Code

Bayesian Factor Models (BFM) are well-established models that decompose the observed variability in a set of mean-zero, independent, and uncorrelated factors (random effects). While Factor Analysis (FA) was introduced in 1904 by Spearman, there has been renewed interest in inferential and computational methods that can adapt to large and complex modern data sets that are now routinely collected in a variety of applications. We provide reproducible, harmonized, and fast software for a variety of recent BFMs that allows the direct comparison of methods and provides a one-stop tutorial for the BFMs and their implementation. We neither endorse nor recommend any of the methods for a particular application; we simply provide a previously unavailable harmonized and reproducible common platform for BFMs.
@article{dunson2026tutorial, title = {Tutorial for {B}ayesian Factor Models}, author = {Dunson, Peter and Crainiceanu, Ciprian M.}, year = {2026}, journal = {arXiv preprint arXiv:2607.11819}, }