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 |
| DOI: | 10.32614/CRAN.package.densEstBayes |
| 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 |
| In views: | Bayesian |
| CRAN checks: | densEstBayes results |
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