First release of mvgam(v1.1.0) to CRAN

The mvgam package has been officially released to CRAN. This package fits 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.

mvgam

R 📦 to fit Dynamic Bayesian Generalised Additive Models for time series analysis and forecasting

mrIML

R 📦 for multivariate (multi-response) interpretable machine learning

MRFcov

R 📦 for network analysis using Conditional Random Fields (CRFs)