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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mvgam to link to this page.
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