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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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