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 (2019) <doi:10.1002/ecy.2403>.

Version: 0.1.4
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, reshape2, rstan (≥ 2.18.2), rstantools (≥ 1.5.1), tibble
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.8), RcppEigen (≥ 0.3.3.3.0), rstan (≥ 2.18.2), StanHeaders (≥ 2.18.0)
Suggests: bayesplot, coda, knitr, parallel, rmarkdown, testthat, viridis
Published: 2020-07-09
Author: Sean C. Anderson [aut, cre], Eric J. Ward [aut], Trustees of Columbia University [cph]
Maintainer: Sean C. Anderson <sean at seananderson.ca>
BugReports: https://github.com/seananderson/glmmfields/issues
License: GPL (≥ 3)
URL: https://github.com/seananderson/glmmfields
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: glmmfields citation info
Materials: NEWS
CRAN checks: glmmfields results

Downloads:

Reference manual: glmmfields.pdf
Vignettes: Spatial GLMs with glmmfields
Package source: glmmfields_0.1.4.tar.gz
Windows binaries: r-devel: glmmfields_0.1.3.zip, r-release: glmmfields_0.1.4.zip, r-oldrel: glmmfields_0.1.3.zip
macOS binaries: r-release: glmmfields_0.1.3.tgz, r-oldrel: glmmfields_0.1.4.tgz
Old sources: glmmfields archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=glmmfields to link to this page.