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glmmfields  

Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling
View on CRAN: Click here


Download and install glmmfields package within the R console
Install from CRAN:
install.packages("glmmfields")

Install from Github:
library("remotes")
install_github("cran/glmmfields")

Install by package version:
library("remotes")
install_version("glmmfields", "0.1.8")



Attach the package and use:
library("glmmfields")
Maintained by
Sean C. Anderson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-06-24
Latest Update: 2023-10-20
Description:
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) .
How to cite:
Sean C. Anderson (2018). glmmfields: Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling. R package version 0.1.8, https://cran.r-project.org/web/packages/glmmfields. Accessed 26 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:44), 0.1.0 (2018-06-24 12:47), 0.1.1 (2018-11-09 15:30), 0.1.2 (2019-03-08 12:30), 0.1.3 (2019-05-18 06:40), 0.1.4 (2020-07-09 07:50), 0.1.7 (2023-03-10 23:50)
Other packages that cited glmmfields R package
View glmmfields citation profile
Other R packages that glmmfields depends, imports, suggests or enhances
Complete documentation for glmmfields
Functions, R codes and Examples using the glmmfields R package
Some associated functions: format_data . glmmfields-package . glmmfields . lognormal . loo . nbinom2 . plot.glmmfields . predict . priors . sim_glmmfields . stan_pars . tidy . 
Some associated R codes: family.R . fitting.R . format_data.R . glmmfields-package.R . methods.R . plot.R . predict.R . priors.R . sim.R . stan_pars.R . stanmodels.R .  Full glmmfields package functions and examples
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