This page lists statistical software that I have developed, mostly from my time as a grad student at UT Dallas.

The geostan R package


Supports a complete spatial analysis workflow: exploratory analysis, modeling, diagnostics, and visualization.

DOI

Spatial regression and econometric models
For data recorded across areal units (states, counties, or census tracts) or networks.
Spatial analysis tools
For visualizing and measuring spatial autocorrelation and map patterns, for exploratory analysis and model diagnostics.
Observational error
Incorporate standard errors (e.g., from American Community Survey estimates) into any geostan model.
Missing and Censored observations
Vital statistics and disease surveillance systems like CDC Wonder censor case counts that fall below a threshold number; geostan can model disease or mortality risk for small areas with censored observations or with missing observations.
The RStan ecosystem
Interfaces easily with many high-quality R packages for Bayesian modeling.
Custom spatial models
Tools for building custom spatial or network models in Stan.
Online documentation
https://connordonegan.github.io/geostan
Package vignette
Spatial data analysis with geostan
Publications

Donegan, Connor (2022). geostan: An R package for Bayesian spatial analysis. The Journal of Open Source Software 7, no. 79: 4716 DOI:10.21105/joss.04716

Donegan, Connor (2021). Building spatial conditional autoregressive (CAR) models in the Stan programming language. OSF Preprints. DOI:10.31219/osf.io/3ey65

Donegan, Connor, Yongwan Chun and Daniel A. Griffith (2021). Modeling community health with areal data: Bayesian inference with survey standard errors and spatial structure International Journal of Environmental Research and Public Health 18, no. 13: 6856. DOI:10.3390/ijerph18136856 Supplementary material: https://github.com/ConnorDonegan/survey-HBM.

Donegan, Connor, Yongwan Chun and Amy E. Hughes (2020). Bayesian estimation of spatial filters with Moran's eigenvectors and hierarchical shrinkage priors. Spatial Statistics 38: 100450. DOI:10.1016/j.spasta.2020.100450 Pre-print URL: https://osf.io/fah3z/

The surveil R package


Models for time trends in mortality or disease incidence, for routine public health tasks. An accessible alternative to joinpoint regression.

Age-standardized rates
Automatically fit and combine age-specific models for directly age-standardized rates.
Percent change analysis
Simple methods for getting cumulative percent change and period-specific percent change statistics.
Professional figures
Publication-quality default graphics
Online documentation
https://connordonegan.github.io/surveil
Package vignette
Modeling time trends for disease monitoring studies
Publications

Donegan, Connor, Amy E Hughes and Simon J Craddock Lee (2022). Colorectal Cancer Incidence, Inequalities, and Prevention Priorities in Urban Texas: Surveillance Study with the "surveil" software package. JMIR Public Health & Surveillance 8, no. 8: e34589 DOI:10.2196/34589 PMID:35972778

Other code

Donegan, Connor and Mitzi Morris (2021). "Flexible functions for ICAR, BYM, and BYM2 models in Stan.” Code repository. https://github.com/ConnorDonegan/Stan-IAR

Functions in the R and Stan programming languages that make it easier to implement the BYM and BYM2 spatial models in RStan. The code addresses multiple challenges that arise when using a disconnected graph structure with the intrinsic conditional autoregressive model. The functions support the construction of custom spatial models in Stan, and are also used by the geostan R package.