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:	0.1.8
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
Published:	2023-10-20
DOI:	10.32614/CRAN.package.glmmfields
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
In views:	MixedModels
CRAN checks:	glmmfields results

Documentation:

Reference manual:	glmmfields.pdf
Vignettes:	Spatial GLMs with glmmfields

Downloads:

Package source:	glmmfields_0.1.8.tar.gz
Windows binaries:	r-devel: glmmfields_0.1.8.zip, r-release: glmmfields_0.1.8.zip, r-oldrel: glmmfields_0.1.8.zip
macOS binaries:	r-release (arm64): glmmfields_0.1.8.tgz, r-oldrel (arm64): glmmfields_0.1.8.tgz, r-release (x86_64): glmmfields_0.1.8.tgz, r-oldrel (x86_64): glmmfields_0.1.8.tgz
Old sources:	glmmfields archive

Linking:

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