Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2022) <doi:10.1111/2041-210X.13974>.
Version: | 1.1.3 |
Depends: | R (≥ 3.6.0) |
Imports: | brms (≥ 2.21.0), methods, mgcv (≥ 1.8-13), insight (≥ 0.19.1), marginaleffects (≥ 0.16.0), Rcpp (≥ 0.12.0), rstan (≥ 2.29.0), posterior (≥ 1.0.0), loo (≥ 2.3.1), rstantools (≥ 2.1.1), bayesplot (≥ 1.5.0), ggplot2 (≥ 2.0.0), parallel, pbapply, mvnfast, purrr, zoo, smooth, dplyr, magrittr, Matrix, rlang |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | scoringRules, matrixStats, cmdstanr (≥ 0.5.0), tweedie, splines2, extraDistr, wrswoR, xts, lubridate, knitr, collapse, rmarkdown, rjags, coda, runjags, usethis, testthat |
Enhances: | gratia (≥ 0.9.0), tibble (≥ 3.0.0), tidyr |
Published: | 2024-09-04 |
DOI: | 10.32614/CRAN.package.mvgam |
Author: | Nicholas J Clark [aut, cre] |
Maintainer: | Nicholas J Clark <nicholas.j.clark1214 at gmail.com> |
BugReports: | https://github.com/nicholasjclark/mvgam/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/nicholasjclark/mvgam, https://nicholasjclark.github.io/mvgam/ |
NeedsCompilation: | yes |
Additional_repositories: | https://mc-stan.org/r-packages/ |
Citation: | mvgam citation info |
Materials: | README NEWS |
In views: | Bayesian, Environmetrics, TimeSeries |
CRAN checks: | mvgam results |
Package source: | mvgam_1.1.3.tar.gz |
Windows binaries: | r-devel: mvgam_1.1.3.zip, r-release: mvgam_1.1.3.zip, r-oldrel: mvgam_1.1.3.zip |
macOS binaries: | r-release (arm64): mvgam_1.1.3.tgz, r-oldrel (arm64): mvgam_1.1.3.tgz, r-release (x86_64): mvgam_1.1.3.tgz, r-oldrel (x86_64): mvgam_1.1.3.tgz |
Old sources: | mvgam archive |
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