CRAN/E | mvgam

mvgam

Multivariate (Dynamic) Generalized Additive Models

Installation

About

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.

Citation mvgam citation info
github.com/nicholasjclark/mvgam
nicholasjclark.github.io/mvgam/
Bug report File report

Key Metrics

Version 1.1.1
R ≥ 3.6.0
Published 2024-05-10 155 days ago
Needs compilation? yes
License MIT
License File
CRAN checks mvgam results

Downloads

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Maintainer

Maintainer

Nicholas J Clark

Authors

Nicholas J Clark

aut / cre

Material

README
NEWS
Reference manual
Package source

In Views

TimeSeries

Additional repos

mc-stan.org/r-packages/

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

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

mvgam archive

Depends

R ≥ 3.6.0
mgcv ≥ 1.8-13
Rcpp ≥ 0.12.0
brms ≥2.17
marginaleffects
insight ≥ 0.19.1
methods

Imports

rstan ≥ 2.29.0
posterior ≥ 1.0.0
loo ≥ 2.3.1
rstantools ≥ 2.1.1
bayesplot ≥ 1.5.0
ggplot2 ≥2.0.0
matrixStats
parallel
pbapply
mvnfast
purrr
zoo
scoringRules
smooth
dplyr
magrittr
Matrix
rlang

Suggests

cmdstanr ≥ 0.5.0
tweedie
splines2
extraDistr
wrswoR
xts
lubridate
knitr
collapse
rmarkdown
rjags
coda
runjags
usethis
testthat

LinkingTo

Rcpp
RcppArmadillo