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) .


glmmfields 0.1.0

  • Initial submission to CRAN.

Reference manual

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0.1.1 by Sean C. Anderson, 3 months ago

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

Documentation:   PDF Manual  

GPL (>= 3) license

Imports rstan, ggplot2, cluster, dplyr, reshape2, mvtnorm, broom, methods, loo, assertthat, nlme, forcats, rstantools

Depends on Rcpp

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

Linking to StanHeaders, rstan, BH, Rcpp, RcppEigen

System requirements: GNU make

See at CRAN