R package spate: Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach. This package provides functionality for spatio-temporal modeling of large data sets. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- RESSTE Network; et al.: Analyzing spatio-temporal data with R: everything you always wanted to know -- but were afraid to ask (2017)
- Bakar, K. Shuvo; Kokic, Philip; Jin, Huidong: Hierarchical spatially varying coefficient and temporal dynamic process models using \textttspTDyn (2016)
- Andrew Finley; Sudipto Banerjee; Alan Gelfand: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (2015) not zbMATH
- Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015) not zbMATH
- Fabio Sigrist; Hans Künsch; Werner Stahel: spate: An R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process (2015) not zbMATH
- Sigrist, Fabio; Künsch, Hans R.; Stahel, Werner A.: A dynamic nonstationary spatio-temporal model for short term prediction of precipitation (2012)