diagnostics

convergence

<p>A list of available diagnostics and links to their individual help pages.</p>

Provides useful tools for both users and developers of packages
for fitting Bayesian models or working with output from Bayesian models.
The primary goals of the package are to:
(a) Efficiently convert between many different useful formats of
draws (samples) from posterior or prior distributions.
(b) Provide consistent methods for operations commonly performed on draws,
for example, subsetting, binding, or mutating draws.
(c) Provide various summaries of draws in convenient formats.
(d) Provide lightweight implementations of state of the art posterior
inference diagnostics. References: Vehtari et al. (2021)
<doi:10.1214/20-BA1221>.

PaulChristian Buerkner

posterior

Tools for Working with Posterior Distributions

Paul-Christian B<c3><bc>rkner

Jonah Gabry

Matthew Kay

Aki Vehtari

M<c3><a5>ns Magnusson

Rok <c4><8c>e<c5><a1>novar

Ben Lambert

Ozan Ad<c4><b1>g<c3><bc>zel

Jacob Socolar

diagnostics function

List of available convergence diagnostics — diagnostics

List of available convergence diagnostics

Function	Description
`ess_basic()`	Basic version of effective sample size
`ess_bulk()`	Bulk effective sample size
`ess_tail()`	Tail effective sample size
`ess_quantile()`	Effective sample sizes for quantiles
`ess_sd()`	Effective sample sizes for standard deviations
`mcse_mean()`	Monte Carlo standard error for the mean
`mcse_quantile()`	Monte Carlo standard error for quantiles
`mcse_sd()`	Monte Carlo standard error for standard deviations
`rhat_basic()`	Basic version of Rhat
`rhat()`	Improved, rank-based version of Rhat
`rstar()`	R* diagnostic

diagnostics: List of available convergence diagnostics

Description

Arguments

Value

Details