| Name : R-spate
| |
| Version : 1.7.5
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp156.1.1
| Date : 2024-06-24 14:10:16
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| Group : Development/Libraries/Other
| Source RPM : R-spate-1.7.5-lp156.1.1.src.rpm
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| Size : 1.42 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach
|
Description :
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. See
Sigrist, Kuensch, and Stahel (2015) < doi:10.1111/rssb.12061> for more
information on the methodology. 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.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
