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) .


glmmfields 0.1.2

  • Make compatible with R 3.6.0 staged installation and latest rstantools.

glmmfields 0.1.1

  • Changed how Stan finds directories / files


  • Add support for random walk year effects with covariates. There are a few specific cases where covariates or the random year effect are not estimable. Examples are:

    1. estimating an intercept and AR year effects (intercept confounded with 1st year effect)
    2. estimating AR year effects and estimating 'phi' — the AR parameter in spatial field (also confounded)
  • Import S3 methods from rstantools instead of rstanarm (#5)

  • Adjust calculation of year index values to better allow for missing time slices

glmmfields 0.1.0

  • Initial submission to CRAN.

Reference manual

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0.1.4 by Sean C. Anderson, a year ago

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Browse source code at

Authors: Sean C. Anderson [aut, cre] , Eric J. Ward [aut] , Trustees of Columbia University [cph]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports assertthat, broom, broom.mixed, cluster, dplyr, forcats, ggplot2, loo, mvtnorm, nlme, reshape2, rstan, rstantools, tibble

Depends on methods, Rcpp

Suggests bayesplot, coda, knitr, parallel, rmarkdown, testthat, viridis

Linking to BH, Rcpp, RcppEigen, rstan, StanHeaders

System requirements: GNU make

See at CRAN