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.
|Author||Simone Padoan[aut, cre] <firstname.lastname@example.org>, Moreno Bevilacqua[aut] <email@example.com>|
|Date of publication||2015-02-01 15:14:09|
|Maintainer||Simone Padoan <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on R-Forge|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.