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.


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install.packages("spaMM")

3.0.0 by François Rousset, 5 months 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, pbapply

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

Linking to Rcpp, RcppEigen


Imported by DHARMa, Infusion, IsoriX, blackbox.


See at CRAN