HGF.jl: a Julia package for Hierarchical Gaussian Filter fitting and simulation.

The Hierarchical Gaussian Filter has been used for several years now as a good middle ground between Bayesian Inference and Reinforcement Learning models when it comes to estimating how an agent updates its believes when presented with new information. Our Julia package will provide a new environment to run such analysis endorsed with a more user friendly structure allowing both a faster and smoother workflow and the possibility to implement bigger and more complex hgf structures just as easily. It also makes it finally possible to use sampling techniques to fit the model parameters once given both the inputs and the responses. After a short introduction to HGF models and Julia, this paper will provide a list explaining the functions making up the package and a couple of usage examples showing the workflow in action both in a testing case and a real research task.