spate (1.5)

0 users

Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach.

http://cran.r-project.org/web/packages/spate

Functionality for spatio-temporal modeling of large data sets is provided. 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.

Maintainer: Fabio Sigrist
Author(s): Fabio Sigrist, Hans R. Kuensch, Werner A. Stahel

License: GPL-2

Uses: mvtnorm, truncnorm

Released 12 months ago.


5 previous versions

Ratings

Overall:

  (0 votes)

Documentation:

  (0 votes)

Log in to vote.

Reviews

No one has written a review of spate yet. Want to be the first? Write one now.


Related packages: RNetCDF, RandomFields, argosfilter, diveMove, lme4, nlme, pastecs, plm, rgl, spBayes, sp, spatstat, splancs, surveillance, tripEstimation, gstat, trip, xts, Stem, crawl(20 best matches, based on common tags.)


Search for spate on google, google scholar, r-help, r-devel.

Visit spate on R Graphical Manual.