mvgam
Multivariate (Dynamic) Generalized Additive Models
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.
- Version1.1.3
- R version≥ 3.6.0
- LicenseMIT
- LicenseLICENSE
- Needs compilation?Yes
- mvgam citation info
- Last release09/04/2024
Documentation
- VignetteFormatting data for use in mvgam
- VignetteForecasting and forecast evaluation in mvgam
- VignetteOverview of the mvgam package
- VignetteN-mixtures in mvgam
- VignetteShared latent states in mvgam
- VignetteTime-varying effects in mvgam
- VignetteState-Space models in mvgam
- MaterialREADME
- MaterialNEWS
- In ViewsBayesian
- In ViewsEnvironmetrics
- In ViewsTimeSeries
Team
Nicholas J Clark
Insights
Last 30 days
This package has been downloaded 1,156 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 42 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 5,789 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do 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.
Data provided by CRAN
Binaries
Dependencies
- Imports18 packages
- Enhances3 packages
- Suggests15 packages
- Linking To2 packages