mvgam: Multivariate (Dynamic) Generalized Additive Models

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: 1.1.2
Depends: R (≥ 3.6.0), brms (≥ 2.17)
Imports: 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
Published: 2024-07-01
DOI: 10.32614/CRAN.package.mvgam
Author: Nicholas J Clark ORCID iD [aut, cre]
Maintainer: Nicholas J Clark <nicholas.j.clark1214 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: mvgam citation info
Materials: README NEWS
In views: Bayesian, TimeSeries
CRAN checks: mvgam results


Reference manual: mvgam.pdf
Vignettes: Formatting data for use in mvgam
Forecasting and forecast evaluation in mvgam
Overview of the mvgam package
N-mixtures in mvgam
Shared latent states in mvgam
Time-varying effects in mvgam
State-Space models in mvgam


Package source: mvgam_1.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvgam_1.1.2.tgz, r-oldrel (arm64): mvgam_1.1.1.tgz, r-release (x86_64): mvgam_1.1.2.tgz, r-oldrel (x86_64): mvgam_1.1.1.tgz
Old sources: mvgam archive


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