mvgam

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.

Last 30 days

This package has been downloaded 1,177 times in the last 30 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 18 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 5,865 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Feb 20, 2025 with 103 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.