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bayesforecast  

Bayesian Time Series Modeling with Stan
View on CRAN: Click here


Download and install bayesforecast package within the R console
Install from CRAN:
install.packages("bayesforecast")

Install from Github:
library("remotes")
install_github("cran/bayesforecast")

Install by package version:
library("remotes")
install_version("bayesforecast", "1.0.5")



Attach the package and use:
library("bayesforecast")
Maintained by
Asael Alonzo Matamoros
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-01-22
Latest Update: 2025-06-05
Description:
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) ; Carpenter et al. (2017) .
How to cite:
Asael Alonzo Matamoros (2021). bayesforecast: Bayesian Time Series Modeling with Stan. R package version 1.0.5, https://cran.r-project.org/web/packages/bayesforecast. Accessed 02 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:20), 0.0.1 (2021-01-22 11:20), 1.0.1 (2021-06-17 12:00)
Other packages that cited bayesforecast R package
View bayesforecast citation profile
Other R packages that bayesforecast depends, imports, suggests or enhances
Complete documentation for bayesforecast
Functions, R codes and Examples using the bayesforecast R package
Some associated functions: AICc . Holt . Hw . LKJ . LocalLevel . SVM . Sarima . aic . air . as.stan . aust . auto.sarima . autoplot.ts . autoplot.varstan . bayes_factor.varstan . bayesforecast-package . beta . bic . birth . bridge_sampler.varstan . cauchy . check_residuals . chisq . demgbp . exponential . extract_stan . fitted.varstan . forecast.varstan . fourier . gamma . garch . get_parameters . get_prior . ggacf . gghist . ggnorm . ggpacf . inverse.chisq . inverse.gamma . ipc . jeffrey . laplace . log_lik.varstan . loglik . loo.varstan . mcmc_plot.varstan . model . naive . normal . oildata . plot.varstan . posterior_epred.varstan . posterior_interval . posterior_predict.varstan . predictive_error.varstan . print.Holt . print.Hw . print.LocalLevel . print.SVM . print.Sarima . print.garch . print.naive . print.ssm . print.varstan . prior_summary.varstan . reexports . report . residuals.varstan . set_prior . ssm . stan_Holt . stan_Hw . stan_LocalLevel . stan_SVM . stan_garch . stan_naive . stan_sarima . stan_ssm . student . summary.varstan . uniform . varstan . waic.varstan . 
Some associated R codes: Birth.R . Fit.R . Misc.R . SVM.R . Sarima.R . auto_sarima.R . autoplot.R . bayes_factor.R . bayesforecast-package.R . ets.R . forecast.R . garch.R . get_params.R . log_lik.R . model.R . naive.R . posterior_intervals.R . posterior_predict.R . predictive_error.R . print.R . prior.R . report.R . ssm.R . stan_models.R . stanmodels.R . summary.R . varstan.R .  Full bayesforecast package functions and examples
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