Author: Simone Padoan <simone.padoan@unibg.it>, Moreno Bevilacqua

Package: CompRandFld
Version: 0.9
Date: 2010-11-30
Title: Composite-likelihood based Analysis of Random Fields
Author: Simone Padoan <simone.padoan@epfl.ch>, Moreno Bevilacqua
<moreno.bevilacqua@unive.it>
Maintainer: Simone Padoan <simone.padoan@epfl.ch>
Depends: R (>= 2.9.0)
Description: The aim of this package is to collect a set of procedures
for the analysis of Random Fields by Composite Likelihood
methods. Spatial analysis often involves dealing with large
dataset. Therefore even simple studies may be too
computationally demanding. Composite likelihood based methods
are emerging as useful tools for mitigating such computational
problems and show satisfactory results when compared with other
techniques such as, for example the tapering method. Moreover,
composite likelihood (and related quantities) have some good
properties similar to those of the standard likelihood.
Suggests: RandomFields
License: GPL (>= 2)
URL: http://people.epfl.ch/simone.padoan
Packaged: 2010-11-30 09:50:17 UTC; simonepadoan
Repository: CRAN
Date/Publication: 2010-11-30 10:37:28

Author: Simone Padoan <simone.padoan@stat.unipd.it>, Moreno Bevilacqua

Author: Simone Padoan <simone.padoan@unibocconi.it>, Moreno Bevilacqua <moreno.bevilacqua@uv.cl>
Description: The aim of this package is to collect a set of procedures for the analysis of Random Fields by Composite Likelihood methods. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood based methods are emerging as useful tools for mitigating such computational problems and show satisfactory results when compared with other techniques such as, for example the tapering method. Moreover, composite likelihood (and related quantities) have some good properties similar to those of the standard likelihood.
Suggests: RandomFields, spam, scatterplot3d, fields, mapproj

Title: Composite-Likelihood Based Analysis of Random Fields
Author: Simone Padoan[aut, cre] <simone.padoan@unibocconi.it>, Moreno Bevilacqua[aut] <moreno.bevilacqua@uv.cl>
Description: A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood.