mvgam: Multivariate (Dynamic) Generalized Additive Models (original) (raw)

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:	1.1.593
Depends:	R (≥ 3.6.0)
Imports:	brms (≥ 2.21.0), methods, mgcv (≥ 1.8-13), insight (≥ 0.19.1), marginaleffects (≥ 0.29.0), 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 (≥ 3.5.0), 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, ggrepel, ggpp, ggarrow, xts, lubridate, knitr, collapse, rmarkdown, rjags, coda, runjags, usethis, testthat, colorspace
Enhances:	gratia (≥ 0.9.0), tidyr
Published:	2025-09-05
DOI:	10.32614/CRAN.package.mvgam
Author:	Nicholas J Clark [aut, cre], KANK Karunarathna [ctb] (ARMA parameterisations and factor models), 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
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

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