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Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, doi:10.1016/j.softx.2022.101016). 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, doi:10.1111/sjos.12492).
Citation | walker citation info |
github.com/helske/walker | |
System requirements | GNU make |
Bug report | File report |
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