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 (2017)
<arXiv:1703.10364> 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.

variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2018,
Statistics & Computing) <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

Author: Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>)
Maintainer: Daniel W. Heck <dheck@uni-marburg.de>
variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019,
posterior samples of the stationary distribution to (a) assess the uncertainty
in the estimated posterior model probabilities and (b) estimate the effective

Description: MCMC estimation for GARCH (1, 1) with Student-t
distribution using the random-walk MetropolisHastings
Packaged: Mon Nov 2 17:11:50 2009; @work

Description: MCMC estimation for GARCH (1, 1) in mean with Student-t
Packaged: Mon Nov 2 18:14:56 2009; @work

Description: MCMC estimation for Box-Cox transformed GARCH (1, 1) with
Student-t distribution using the random-walk MetropolisHastings
Packaged: Mon Nov 2 18:21:32 2009; @work

Title: Prepare MCMCtree Analyses and Plot Bayesian Divergence Time
Description: Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007)<doi:10.1093/molbev/msm088> and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with either using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. The package also gives options to include the geological timescale and other details alongside the plotted phylogeny. All plotting functions these are applicable with output from any Bayesian software, not just 'MCMCtree'.
Imports: methods, utils, stats, grDevices, graphics
Packaged: 2019-03-23 14:55:43 UTC; markputtick