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

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 ORCID iD [aut, cre]
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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