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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 
[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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https://CRAN.R-project.org/package=densEstBayes
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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