spaMM: 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). 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 <doi:10.1093/biomet/88.4.987>) and Laplace approximation.

Version: 2.6.1
Depends: R (≥ 3.2.0)
Imports: methods, stats, graphics, Matrix, MASS, proxy, Rcpp (≥ 0.12.10), nlme, nloptr, pbapply
LinkingTo: Rcpp, RcppEigen
Suggests: maps, testthat, lme4, rsae, rcdd, pedigreemm, minqa, lpSolveAPI (≥, foreach, multilevel, Infusion (≥ 1.3.0), IsoriX (≥ 0.8.1), blackbox (≥ 1.1.25), gmp
Published: 2019-01-14
Author: François Rousset ORCID iD [aut, cre, cph], Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol ORCID iD [aut], GSL authors [ctb] (src/gsl_bessel.*)
Maintainer: François Rousset <francois.rousset at>
License: CeCILL-2
NeedsCompilation: yes
Citation: spaMM citation info
Materials: NEWS
In views: Spatial
CRAN checks: spaMM results


Reference manual: spaMM.pdf
Package source: spaMM_2.6.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: spaMM_2.6.1.tgz, r-oldrel: spaMM_2.5.11.tgz
Old sources: spaMM archive

Reverse dependencies:

Reverse imports: blackbox, DHARMa, Infusion, IsoriX


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