walker: Bayesian Generalized Linear Models with Time-Varying Coefficients
Jouni Helske · 2017
Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, ). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, ).