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 |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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