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 |