Composite-Likelihood Based Analysis of Random Fields
Simone Padoan[aut, cre] <simone.padoan@unibocconi.it>, Moreno Bevilacqua[aut] <moreno.bevilacqua@uv.cl>
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.

RandomFields, spam, scatterplot3d, fields, mapproj, methods,
maps

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Simone Padoan [cre, aut],
Boris Beranger [aut],
Moreno Bevilacqua [aut],
Alan Genz [ctb] (Author of included MVNDST fragment)

Simone Padoan <simone.padoan@unibocconi.it>, Moreno Bevilacqua
<moreno.bevilacqua@uv.cl>

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Regression methodologies with compositional covariates, including (1) sparse log-contrast regression with compositional covariates proposed by Lin et al. (2014) <doi:10.1093/biomet/asu031>, and (2) sparse log-contrast regression with functional compositional predictors proposed by Sun et al. (2020) <arXiv:1808.02403>.
Rcpp, MASS, plyr, splines, fda, orthogonalsplinebasis, methods