# Bayesian spatially varying coefficient models in the spBayes R package

## TL;DR

This paper introduces new spatially varying coefficient modeling capabilities in the spBayes R package, enabling more flexible spatial regression analysis with improved computational efficiency and parallelization.

## Contribution

The paper extends spBayes to include spatially varying coefficients with Gaussian process effects, incorporating efficient MCMC algorithms and parallelization features.

## Key findings

- Enhanced modeling flexibility for spatial data analysis.
- Improved computational efficiency through OpenMP parallelization.
- Successful application to PM10 pollution data in Europe.

## Abstract

This paper describes and illustrates new functionality for fitting spatially varying coefficients models in the spBayes (version 0.4-2) R package. The new spSVC function uses a computationally efficient Markov chain Monte Carlo algorithm and extends current spBayes functions, that fit only space-varying intercept regression models, to fit independent or multivariate Gaussian process random effects for any set of columns in the regression design matrix. Newly added OpenMP parallelization options for spSVC are discussed and illustrated, as well as helper functions for joint and point-wise prediction and model fit diagnostics. The utility of the proposed models is illustrated using a PM10 analysis over central Europe.