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.
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install.packages('spaMM')
2.6.1
a month ago
François Rousset
CeCILL-2
R (>= 3.2.0)
methods, stats, graphics, Matrix, MASS, proxy, Rcpp (>= 0.12.10), nlme, nloptr, pbapply
maps, testthat, lme4, rsae, rcdd, pedigreemm, minqa, lpSolveAPI (>= 5.5.0.14), foreach, multilevel, Infusion (>= 1.3.0), IsoriX (>= 0.8.1), blackbox (>= 1.1.25), gmp