DESCRIPTION

Package: mvgam
Title: Multivariate Bayesian Generalised Additive Models for discrete time series
Version: 1.0.0
Authors@R: 
    person(given = "Nicholas",
           family = "Clark",
           role = c("aut", "cre"),
           email = "nicholas.j.clark1214@gmail.com")
Description: This package is for fitting Bayesian Generalised Additive Models to sets of discrete time series. 
             The primary purpose of the package is to build a version of dynamic factor models that incorporates 
             the flexibility of GAMs in the linear predictor and latent dynamic trend components that are useful 
             for time series analysis and forecasting. Estimation is performed using Markov Chain Monte Carlo (MCMC) with
             either the Gibbs sampling software JAGS or with Hamiltonian Monte Carlo in the software Stan. The package also includes 
             utilities for online updating of  forecast distributions with a recursive particle filter that 
             uses sequential importance sampling to assimilate new observations as they become available.
Maintainer: Nicholas J Clark <nicholas.j.clark1214@gmail.com>
License: MIT + file LICENSE
Depends: R (>= 3.6.0), mgcv, parallel
Imports: 
    pbapply,
    rjags,
    runjags,
    tweedie,
    lubridate,
    MASS,
    purrr,
    zoo,
    smooth,
    coda,
    MCMCpack,
    runjags,
    MCMCvis,
    dplyr,
    magrittr
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr