Package: MCMCprecision
Type: Package
Title: Precision of Discrete Parameters in Transdimensional MCMC
Version: 0.4.0
Date: 2019-12-04
Authors@R: c(person("Daniel W.","Heck", 
                    email = "dheck@uni-marburg.de",
                    role = c("aut","cre"), 
                    comment = c(ORCID = "0000-0002-6302-9252")))
Author: Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>)
Maintainer: Daniel W. Heck <dheck@uni-marburg.de>
Description: 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.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.0)
Imports: Rcpp, parallel, utils, stats, Matrix, combinat
Suggests: testthat, R.rsp
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppEigen
RoxygenNote: 7.0.2
URL: https://github.com/danheck/MCMCprecision
VignetteBuilder: R.rsp
NeedsCompilation: yes
Packaged: 2019-12-04 21:16:38 UTC; daniel
Repository: CRAN
Date/Publication: 2019-12-05 09:00:09 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2020-05-14 19:01:56 UTC; windows
Archs: x64
