# mvgam

R ðŸ“¦ to fit Dynamic Bayesian Generalised Additive Models for time series analysis and forecasting

R ðŸ“¦ to fit Dynamic Bayesian Generalised Additive Models for time series analysis and forecasting

Example of how to simulate binary observations of an imperfectly observed data generating process (i.e. binary measurements that are made with error) and use Stan to estimate parameters of the model in a Bayesian framework.

Switching to spatial regression modeling, I here show how to simulate discrete observations over a latent Gaussian Process spatial autocorrelation function. I then demonstrate how to use Stan to estimate parameters of the model using a spatial distance matrix as input.