Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) <arXiv:2009.06182>.
https://anaconda.org/cf-staging/r-densestbayes/badges/version.svg
https://anaconda.org/cf-staging/r-densestbayes/badges/latest_release_date.svg
https://anaconda.org/cf-staging/r-densestbayes/badges/latest_release_relative_date.svg
https://anaconda.org/cf-staging/r-densestbayes/badges/platforms.svg
https://anaconda.org/cf-staging/r-densestbayes/badges/license.svg
https://anaconda.org/cf-staging/r-densestbayes/badges/downloads.svg