MCMCprecision: Precision of Discrete Parameters in Transdimensional MCMC
Estimates the precision of transdimensional Markov chain Monte Carlo
(MCMC) output, which is often used for Bayesian analysis of models with different
dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible
jump MCMC) relies on sampling a discrete model-indicator variable to estimate
the posterior model probabilities. If only few switches occur between the models,
precision may be low and assessment based on the assumption of independent
samples misleading. Based on the observed transition matrix of the indicator
variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019,
Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws
posterior samples of the stationary distribution to (a) assess the uncertainty
in the estimated posterior model probabilities and (b) estimate the effective
sample size of the MCMC output.
| Version: |
0.4.0 |
| Depends: |
R (≥ 3.0.0) |
| Imports: |
Rcpp, parallel, utils, stats, Matrix, combinat |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppProgress, RcppEigen |
| Suggests: |
testthat, R.rsp |
| Published: |
2019-12-05 |
| DOI: |
10.32614/CRAN.package.MCMCprecision |
| Author: |
Daniel W. Heck 
[aut, cre] |
| Maintainer: |
Daniel W. Heck <dheck at uni-marburg.de> |
| License: |
GPL-3 |
| URL: |
https://github.com/danheck/MCMCprecision |
| NeedsCompilation: |
yes |
| Citation: |
MCMCprecision citation info |
| Materials: |
NEWS |
| CRAN checks: |
MCMCprecision results |
Documentation:
Downloads:
Reverse dependencies:
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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.