Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling R package glmmfields version 0.1.8 CRAN Task View: MixedModels (https://CRAN.R-project.org/view=MixedModels) Implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. 'glmmfields' uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with 'Stan'. References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>. Software Depends: methods, R (>= 3.4.0), Rcpp (>= 0.12.18) Imports: assertthat, broom, broom.mixed, cluster, dplyr (>= 0.8.0), forcats, ggplot2 (>= 2.2.0), loo (>= 2.0.0), mvtnorm, nlme, RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.26.0), rstantools (>= 2.1.1), tibble LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.8), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: bayesplot, coda, knitr, parallel, rmarkdown, testthat, viridis Sean C. Anderson <sean@seananderson.ca> Comprehensive R Archive Network (CRAN) Sean C. Anderson [aut, cre], Eric J. Ward [aut], Trustees of Columbia University [cph] GPL (>= 3) 2023-10-20 application/tgz https://CRAN.R-project.org/package=glmmfields doi:10.32614/CRAN.package.glmmfields