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>.

Package details

AuthorNicholas J Clark [aut, cre] (<>)
MaintainerNicholas J Clark <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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mvgam documentation built on July 1, 2024, 9:07 a.m.