_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-densestbayes 1.0-2.2
Propagated dependencies: r-bh@1.87.0-1 r-mass@7.3-65 r-nlme@3.1-168 r-rcpp@1.1.0 r-rcpparmadillo@15.2.2-1 r-rcppeigen@0.3.4.0.2 r-rcppparallel@5.1.11-1 r-rstan@2.32.7 r-rstantools@2.5.0 r-stanheaders@2.32.10
Channel: guix
Location: gnu/packages/cran.scm (gnu packages cran)
Home page: https://cran.r-project.org/package=densEstBayes
Licenses: GPL 2+
Build system: r
Synopsis: Density estimation via Bayesian inference engines
Description:

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.

Total results: 1