mvgam

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

Using Stan for logistic regressions with detection error

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.

Using Stan to model geostatistical count data with distance matrices

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.