rstap: An R Package for Spatial Temporal Aggregated Predictor Models

2018/12/26 by Adam Peterson, Peterson, Adam, Brisa N. Sánchez +2 · 2 citations
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #demographic modeling and climate adaptation #stat.ME

paper · pdf · doi:10.48550/arxiv.1812.10208

20 pages 12 figures

arxiv created 2018/12/26 · openalex publication_date 2018/12/26 · arxiv updated 2018/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language Stan. A variety of distributions and link functions are supported, allowing users to fit this extension to the generalized linear model with both independent and correlated outcomes.

Citations

• Monte Carlo sampling methods using Markov chains and their applications
• Modeling exposure–lag–response associations with distributed lag non‐linear models
• Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
• A Conceptual Introduction to Hamiltonian Monte Carlo

Cited by

• Heterogeneous Effects in the Built Environment
• Spatial Temporal Aggregated Predictors to Examine Built Environment Health Effects

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