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: |
0.1.4 |
| 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, reshape2, rstan (≥ 2.18.2), rstantools (≥ 1.5.1), tibble |
| LinkingTo: |
BH (≥ 1.66.0), Rcpp (≥ 0.12.8), RcppEigen (≥ 0.3.3.3.0), rstan (≥ 2.18.2), StanHeaders (≥ 2.18.0) |
| Suggests: |
bayesplot, coda, knitr, parallel, rmarkdown, testthat, viridis |
| Published: |
2020-07-09 |
| 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 |
| CRAN checks: |
glmmfields results |