spaMM: Mixed-Effect Models, Particularly Spatial Models

Inference in mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta-Binomial). 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: 2.3.0
Depends: R (≥ 3.2.0)
Imports: methods, stats, graphics, Matrix, MASS, proxy, Rcpp (≥ 0.12.10), nlme, nloptr
LinkingTo: Rcpp, RcppEigen
Suggests: maps, testthat, lme4, rsae, rcdd, e1071, foreach, pedigreemm, minqa
Published: 2018-01-18
Author: François Rousset [aut, cre, cph], Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [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_2.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: spaMM_2.3.0.tgz
OS X Mavericks binaries: r-oldrel: spaMM_2.2.0.tgz
Old sources: spaMM archive

Reverse dependencies:

Reverse imports: blackbox, Infusion, IsoriX


Please use the canonical form to link to this page.