glmmfields: Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling

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 (2018) <doi:10.1002/ecy.2403>.

Version: 0.1.0
Depends: R (≥ 3.3.0), Rcpp (≥ 0.12.0)
Imports: rstan (≥ 2.16.0), ggplot2 (≥ 2.2.0), cluster, dplyr (≥ 0.7.0), reshape2, mvtnorm, broom, methods, loo, assertthat, nlme, forcats, rstanarm
LinkingTo: StanHeaders (≥ 2.16.0), rstan (≥ 2.16.0), BH (≥ 1.62.0), Rcpp (≥ 0.12.0), RcppEigen
Suggests: testthat, parallel, bayesplot, knitr, rmarkdown, viridis, coda
Published: 2018-06-24
Author: Sean C. Anderson [aut, cre], Eric J. Ward [aut], Trustees of Columbia University [cph]
Maintainer: Sean C. Anderson <sean at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: glmmfields citation info
Materials: NEWS
CRAN checks: glmmfields results


Reference manual: glmmfields.pdf
Vignettes: Spatial GLMs with glmmfields
Package source: glmmfields_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: glmmfields_0.1.0.tgz, r-oldrel: glmmfields_0.1.0.tgz


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