spaMM: Mixed Models, Particularly Spatial GLMMs

Inference in mixed models, including GLMMs with spatial correlations and models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroscedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.

Version: 1.10.0
Depends: R (≥ 3.1.0)
Imports: methods, stats, graphics, Matrix, MASS, lpSolveAPI (≥, proxy, geometry (≥ 0.3.6), Rcpp (≥ 0.11.0), nlme, mvtnorm, nloptr
LinkingTo: Rcpp, RcppEigen
Suggests: maps, testthat, lme4, rsae, ff, rasterVis, rgdal, rcdd
Published: 2016-09-05
Author: François Rousset [aut, cre, cph], Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [ctb], Dirk Eddelbuettel [ctb] (ziggurat rnorm sources), 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_1.10.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: spaMM_1.10.0.tgz, r-oldrel: spaMM_1.10.0.tgz
Old sources: spaMM archive

Reverse dependencies:

Reverse imports: blackbox, Infusion, IsoriX


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