Package: spate
Type: Package
Title: Spatio-temporal modeling of large data using a spectral SPDE
        approach
Version: 1.0
Date: 2012-11-29
Author: Fabio Sigrist, Hans R. Kuensch, Werner A. Stahel
Maintainer: Fabio Sigrist <sigrist@stat.math.ethz.ch>
Depends: mvtnorm, truncnorm
SystemRequirements: fftw3 (>= 3.1.2)
Description: This is an R package for spatio-temporal modeling of large
        data sets. It provides tools for modeling of Gaussian processes
        in space and time 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
        parametrizations 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.
License: GPL-2
Packaged: 2012-11-29 13:53:02 UTC; fsigrist
Repository: CRAN
Date/Publication: 2012-11-29 18:26:54
