Mixed-Effect Models, with or without Spatial Random Effects

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. 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, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 ) are also implemented.


Reference manual

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3.9.13 by Fran├žois Rousset, 22 days 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, memoise

Enhances multcomp, RLRsim

Linking to Rcpp, RcppEigen

Imported by Infusion, IsoriX, blackbox.

Suggested by DHARMa.

See at CRAN