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>.

Version: 1.0-1
Depends: R (≥ 3.5.0)
Imports: MASS, nlme, Rcpp, methods, rstan
LinkingTo: BH, Rcpp, RcppArmadillo, RcppEigen, RcppParallel, StanHeaders, rstan
Published: 2020-09-30
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
CRAN checks: densEstBayes results

Downloads:

Reference manual: densEstBayes.pdf
Vignettes: densEstBayes User Manual
Package source: densEstBayes_1.0-1.tar.gz
Windows binaries: r-devel: densEstBayes_1.0-1.zip, r-release: densEstBayes_1.0-1.zip, r-oldrel: densEstBayes_1.0-1.zip
macOS binaries: r-release: densEstBayes_1.0-1.tgz, r-oldrel: densEstBayes_1.0-1.tgz

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