BSTFA: An R Package for Efficient Bayesian Spatio-temporal Factor Analysis

Factor analysis methods are widely used for exploring latent characteristics of a random process. Spatio-temporal factor analysis extends this approach to account for spatial and temporal dependencies in spatio-temporal data. By modeling these dependencies, the estimated processes can be interpolated to unobserved locations. However, Bayesian spatio-temporal models are notoriously computationally burdensome, limiting their practical use. To address this, we developed the BSTFA package in R to automatically fit an efficient Bayesian spatio-temporal factor analysis model using dimension-reduced basis functions. The BSTFA package is user-friendly, computationally fast, and provides a powerful tool for modeling and interpreting spatio-temporal dependencies. We demonstrate its utility with a case study modeling PM 2.5 levels across the state of California for 25 years.

Bayesian modeling

R Package

Spatio-temporal data

MCMC

Latent analysis

Basis functions 

Section on Statistics and the Environment