Mixed-Effect Models, Particularly Spatial Models

Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Both classical geostatistical models, and Markov random field models on irregular grids, can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a generalized linear mixed model. Various approximations of likelihood or restricted likelihood are implemented, in particular h-likelihood (Lee and Nelder 2001 ) and Laplace approximation.


Reference manual

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3.1.2 by François Rousset, 6 days ago

https://www.r-project.org, http://kimura.univ-montp2.fr/~rousset/spaMM.htm

Browse source code at https://github.com/cran/spaMM

Authors: François Rousset [aut, cre, cph] , Jean-Baptiste Ferdy [aut, cph] , Alexandre Courtiol [aut] , GSL authors [ctb] (src/gsl_bessel.*)

Documentation:   PDF Manual  

Task views: Analysis of Spatial Data

CeCILL-2 license

Imports methods, stats, graphics, Matrix, MASS, proxy, Rcpp, nlme, nloptr, minqa, pbapply, crayon, gmp

Suggests maps, testthat, lme4, rsae, rcdd, pedigreemm, lpSolveAPI, foreach, multilevel, Infusion, IsoriX, blackbox

Enhances multcomp

Linking to Rcpp, RcppEigen

Imported by DHARMa, Infusion, IsoriX, blackbox.

See at CRAN