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
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- Imports18 packages
- Enhances3 packages
- Suggests15 packages
- Linking To2 packages