spaMM (1.9.16)

Mixed Models, Particularly Spatial GLMMs.

http://www.r-project.org
http://kimura.univ-montp2.fr/~rousset/spaMM.htm
http://cran.r-project.org/web/packages/spaMM

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.

Maintainer: Franois Rousset
Author(s): Franois Rousset [aut, cre, cph], Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [ctb], Dirk Eddelbuettel [ctb] (ziggurat rnorm sources), GSL authors [ctb] (src/gsl_bessel.*)

License: CeCILL-2

Uses: geometry, lpSolveAPI, MASS, Matrix, mvtnorm, nlme, nloptr, proxy, Rcpp, ff, lme4, maps, rcdd, rgdal, testthat, rasterVis, rsae

Released over 3 years ago.