Hermann, Simone2016-06-072016-06-072016http://hdl.handle.net/2003/3506610.17877/DE290R-17114In many applications, stochastic processes are used for modeling. Bayesian analysis is a strong tool for inference as well as for prediction. We here present an R package for a large class of models, all based on the definition of a jump diffusion with a non-homogeneous Poisson process. Special cases, as the Poisson process itself, a general diffusion process or a hierarchical (mixed) diffusion model, are considered. The package is a general tool box, because it is based on the stochastic differential equation, approximated with the Euler scheme. Functions for simulation, estimation and prediction are provided for each considered model.enDiscussion Paper / SFB823;28, 2016Bayesian estimationstochastic differential equation(jump) diffusionhidden Markov modelhierarchical (mixed) modelEuler-Maruyama approximation310330620working paper