Mixed-Effect Models, Particularly Spatial Models

Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations, multivariate responses, and models with non-Gaussian random effects (e.g., Beta). Various approximations of likelihood or restricted likelihood are implemented, in particular Laplace approximation and h-likelihood (Lee and Nelder 2001 ). Both classical geostatistical models, and Markov random field models on irregular grids (as considered in the 'INLA' package, < https://www.r-inla.org>), can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model.


Reference manual

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3.8.9 by Fran├žois Rousset, a month ago

https://www.r-project.org, https://gitlab.mbb.univ-montp2.fr/francois/spamm-ref

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

Authors: Fran├žois 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, ROI, boot

Suggests maps, testthat, lme4, rsae, rcdd, pedigreemm, foreach, future, future.apply, multilevel, Infusion, IsoriX, blackbox, RSpectra, ROI.plugin.glpk

Enhances multcomp, RLRsim

Linking to Rcpp, RcppEigen

Imported by Infusion, IsoriX, blackbox.

Suggested by DHARMa.

See at CRAN