[aut, cre]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
[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 |
| 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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