Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build 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 (2023) <doi:10.1111/2041-210X.13974>.
| Version: |
1.1.51 |
| 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), mvnfast, purrr, dplyr, magrittr, rlang, generics, tibble (≥ 3.0.0), patchwork (≥ 1.2.0) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
scoringRules, matrixStats, cmdstanr (≥ 0.5.0), tweedie, splines2, extraDistr, corpcor, wrswoR, xts, lubridate, knitr, collapse, rmarkdown, rjags, coda, runjags, usethis, testthat |
| Enhances: |
gratia (≥ 0.9.0), tidyr |
| Published: |
2025-03-14 |
| DOI: |
10.32614/CRAN.package.mvgam |
| Author: |
Nicholas J Clark 
[aut, cre],
Sarah Heaps [ctb]
(VARMA parameterisations),
Scott Pease [ctb]
(broom enhancements),
Matthijs Hollanders
[ctb] (ggplot
visualizations) |
| 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 |