mvgam
Multivariate (Dynamic) Generalized Additive Models
v1.1.594
·
Jan 20, 2026
·
MIT + file LICENSE
Description
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build 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 (2023) <doi:10.1111/2041-210X.13974>.
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NOTE 11 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
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new
1.1.594
Mar 10, 2026
new
1.1.0
May 5, 2024