bayesforecast: Bayesian Time Series Modeling with Stan

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Version: 0.0.1
Depends: R (≥ 4.0.0)
Imports: bayesplot (≥ 1.5.0), methods, gridExtra, ggplot2, forecast, loo (≥ 2.2.0), Rcpp (≥ 0.12.0), rstan (≥ 2.18.1), rstantools (≥ 2.0.0), RcppParallel (≥ 5.0.1), bridgesampling (≥ 0.3-0), MASS, StanHeaders, astsa, lubridate, prophet, zoo
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), RcppEigen (≥ 0.3.3.3.0), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Suggests: knitr, rmarkdown, ggfortify
Published: 2021-01-22
Author: Asael Alonzo Matamoros [aut, cre], Cristian Cruz Torres [aut], Rob Hyndman [ctb], Mitchell O'Hara-Wild [ctb]
Maintainer: Asael Alonzo Matamoros <asael.alonzo at gmail.com>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: bayesforecast citation info
Materials: README NEWS
In views: TimeSeries
CRAN checks: bayesforecast results

Downloads:

Reference manual: bayesforecast.pdf
Package source: bayesforecast_0.0.1.tar.gz
Windows binaries: r-devel: bayesforecast_0.0.1.zip, r-release: bayesforecast_0.0.1.zip, r-oldrel: not available
macOS binaries: r-release: bayesforecast_0.0.1.tgz, r-oldrel: not available

Reverse dependencies:

Reverse depends: bayesmodels

Linking:

Please use the canonical form https://CRAN.R-project.org/package=bayesforecast to link to this page.