rstan (>= 2.18.1), ggplot2 (>= 2.2.0), cluster, dplyr (>=
0.7.0), reshape2, mvtnorm, broom, methods, loo, assertthat,
nlme, forcats, rstantools

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>.
rstan (>= 2.18.2), ggplot2 (>= 2.2.0), rstantools (>= 1.5.1),
cluster, dplyr (>= 0.8.0), reshape2, mvtnorm, broom, loo (>=
2.0.0), assertthat, nlme, forcats

c(person(given = c("Sean", "C."),
family = "Anderson",
role = c("aut", "cre"),
email = "sean@seananderson.ca"),
person(given = c("Eric", "J."),
family = "Ward",
role = "aut"),
person(given = "Trustees of",
family = "Columbia University",
role = "cph"))
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>.
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
bayesplot, coda, knitr, parallel, rmarkdown, testthat,
viridis

Specification of Generalised Linear Mixed Models
c(person("Sam", "Watson", email = "S.I.Watson@bham.ac.uk",
role = c("aut", "cre")),
person("Yi", "Pan", email = "ypan1988@gmail.com",
role = c("aut")))
Specification of generalised linear mixed models using the 'R6' object-orientated class system. The package provides classes 'Covariance', 'MeanFunction' and 'Model', which allow for flexible specification of generalised linear mixed models, as well as functionality to produce relevant matrices, values, and analyses.
See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a detailed manual.
Rcpp (>= 0.12.0), RcppArmadillo, RcppParallel (>= 5.0.1)