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) 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.


MCMCprecision 0.3.9

  • Updated citation and vignette: Paper in Statistics & Computing (doi:10.1007/s11222-018-9828-0)

MCMCprecision 0.3.8

  • Code refactoring
  • Renamed functions: -> transitions; -> rmarkov; dirichlet.mle -> fit_dirichlet ; stationary.mle -> stationary_mle ; best.k -> best_models
  • Added unit tests
  • Fixed bugs for transitions() of multiple-chain sequences and multiple CPUs in stationary()

MCMCprecision 0.3.6

  • Fixed WARNING: Found ‘__assert_fail’, possibly from ‘assert’ (C)

MCMCprecision 0.3.5

  • Registered C++ routines
  • Improved Description file

MCMCprecision 0.3.3

  • Alternative method to compute eigenvectors: RcppEigen package
  • Improved starting values for Dirichlet estimation algorithm
  • Maximum likelihood estimation of stationary distribution: stationary.mle()
  • Changed default prior to epsilon=1/M (M= number of sampled models)
  • Changed default method to compute eigenvalue decomposition to RcppArmadillo (method="arma")

MCMCprecision 0.3.0

  • Improved estimation of Dirichlet parameters to get effective sample size (C++ version of fixed-point algorithm by Mink, 2000)
  • New function best.k() to get summary for the k models with highest posterior model probability
  • Exports function rdirichlet()
  • Updated licence: GPL-3 (instead of GPL-2)

MCMCprecision 0.2.1

  • New function best.k() to assess estimation uncertainty for the k models with the highest posterior model probabilities

MCMCprecision 0.2.0

  • Implementations with RcppArmadillo::eig_gen and base::eigen

Reference manual

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0.4.0 by Daniel W. Heck, a year ago

Browse source code at

Authors: Daniel W. Heck [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, parallel, utils, stats, Matrix, combinat

Suggests testthat, R.rsp

Linking to Rcpp, RcppArmadillo, RcppProgress, RcppEigen

See at CRAN