Package: densEstBayes 1.0-2.2

densEstBayes: Density Estimation via Bayesian Inference Engines

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>.

Authors:Matt P. Wand [aut, cre]

densEstBayes_1.0-2.2.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
densEstBayes/json (API)

# Install 'densEstBayes' in R:
install.packages('densEstBayes', repos = c('https://mattwand.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

3.82 score 9 packages 15 scripts 1.6k downloads 6 exports 51 dependencies

Last updated from:fe67eca347. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK263
linux-devel-x86_64OK286
source / vignettesOK364
linux-release-arm64OK309
linux-release-x86_64OK280
macos-release-arm64OK210
macos-release-x86_64OK346
macos-oldrel-arm64OK221
macos-oldrel-x86_64OK604
windows-develOK334
windows-releaseOK329
windows-oldrelOK314
wasm-releaseFAIL158

Exports:checkChainsdensEstBayesdensEstBayes.controldensEstBayesVignettedMarronWandrMarronWand

Dependencies:abindbackportsBHcallrcheckmateclicpp11descdistributionalfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSmatrixStatsnlmenumDerivpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstanrstantoolsS7scalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr

densEstBayes User Manual

Rendered frommanual.Rnwusingutils::Sweaveon May 10 2026.

Last update: 2020-09-30
Started: 2020-09-30