Probabilistic programming languages and packages such as Stan, PyMC, Turing.jl and brms have done a lot to make Bayesian inference more accessible to applied researchers. However, there are still several roadblocks to more “automatic” reliable Bayesian inference for general models, such as multilevel hierarchical models or discretized Gaussian process models. We aim to remove one of the roadblocks by integrating automatic, model specific nonlinear reparametrizations for a subset of generalized non-linear multivariate multilevel models into the popular brms package. We will present some applied examples benefitting from our method, including epidemiological time series analysis and discretized Gaussian process regression.
