spaMM: Mixed-Effect Models, with or without Spatial Random Effects

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models, and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

Getting started

Package details

AuthorFrançois Rousset [aut, cre, cph] (<https://orcid.org/0000-0003-4670-0371>), Jean-Baptiste Ferdy [aut, cph], Alexandre Courtiol [aut] (<https://orcid.org/0000-0003-0637-2959>), GSL authors [ctb] (src/gsl_bessel.*)
MaintainerFrançois Rousset <francois.rousset@umontpellier.fr>
LicenseCeCILL-2
Version3.9.13
URL https://www.r-project.org https://gitlab.mbb.univ-montp2.fr/francois/spamm-ref
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("spaMM")

Try the spaMM package in your browser

Any scripts or data that you put into this service are public.

spaMM documentation built on Oct. 3, 2021, 1:06 a.m.