bayesforecast

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.

Last 30 days

This package has been downloaded 433 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 23 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 5,628 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Jul 25, 2024 with 60 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.