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.

本文描述并说明了在spBayes（0.4-2版）R包中拟合空间变化系数模型的新功能。新的spSVC函数使用计算效率高的Markov链蒙特卡罗算法，并扩展了仅适用于时空截距回归模型的当前spBayes函数，以拟合回归设计矩阵中任何列集的独立或多元高斯过程随机效应。讨论和说明了针对spSVC的新添加的OpenMP并行化选项，以及用于联合和逐点预测以及模型拟合诊断的辅助函数。使用PM ₁₀说明了所建议模型的实用性 中欧的分析。