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) <doi:10.48550/arXiv.2009.06182>.

Version: 1.0-2.2
Depends: R (≥ 3.5.0)
Imports: MASS, nlme, Rcpp, methods, rstan, rstantools
LinkingTo: BH, Rcpp, RcppArmadillo, RcppEigen, RcppParallel, StanHeaders, rstan
Published: 2023-03-31
Author: Matt P. Wand ORCID iD [aut, cre]
Maintainer: Matt P. Wand <matt.wand at uts.edu.au>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: GNU make
In views: Bayesian
CRAN checks: densEstBayes results

Documentation:

Reference manual: densEstBayes.pdf
Vignettes: densEstBayes User Manual

Downloads:

Package source: densEstBayes_1.0-2.2.tar.gz
Windows binaries: r-devel: densEstBayes_1.0-2.2.zip, r-release: densEstBayes_1.0-2.2.zip, r-oldrel: densEstBayes_1.0-2.2.zip
macOS binaries: r-release (arm64): densEstBayes_1.0-2.2.tgz, r-oldrel (arm64): densEstBayes_1.0-2.2.tgz, r-release (x86_64): densEstBayes_1.0-2.2.tgz
Old sources: densEstBayes archive

Reverse dependencies:

Reverse imports: reldist
Reverse suggests: sspse

Linking:

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