RBesT-package

The RBesT tools are designed to support in the derivation of
parametric informative priors, asses design characeristics and
perform analyses. Supported endpoints include normal, binary and
Poisson.

Tool-set to support Bayesian evidence synthesis.
This includes meta-analysis, (robust) prior derivation
from historical data, operating characteristics
and analysis (1 and 2 sample cases).

Sebastian Weber

RBesT

R Bayesian Evidence Synthesis Tools

Novartis Pharma AG

Beat Neuenschwander

Heinz Schmidli

Baldur Magnusson

Yue Li

Satrajit Roychoudhury

Trustees of Columbia University

RBesT-package function

<table><tr><td>
Option </td>
<td> Default </td>
<td> Description </td>
</tr><tr><td>
<code>RBesT.MC.warmup</code> </td>
<td> 2000 </td>
<td> MCMC warmup iterations </td>
</tr><tr><td>
<code>RBesT.MC.iter</code> </td>
<td> 6000 </td>
<td> total MCMC iterations </td>
</tr><tr><td>
<code>RBesT.MC.chains</code> </td>
<td> 4 </td>
<td> MCMC chains</td>
</tr><tr><td>
<code>RBesT.MC.thin</code> </td>
<td> 4 </td>
<td> MCMC thinning </td>
</tr><tr><td>
<code>RBesT.MC.control</code> </td>
<td> <code>list(adapt_delta=0.99,</code> </td>
<td> sets <code>control</code> argument for Stan call</td>
</tr><tr><td>
 </td>
<td> <code>stepsize=0.01,</code> </td>
<td> </td>
</tr><tr><td>
 </td>
<td> <code>max_treedepth=20)</code> </td>
<td> </td>
</tr><tr><td>
<code>RBesT.MC.ncp</code> </td>
<td> 1 </td>
<td> parametrization: 0=CP, 1=NCP, 2=Automatic </td>
</tr><tr><td>
<code>RBesT.MC.init</code> </td>
<td> 1 </td>
<td> range of initial uniform [-1,1] is the default </td>
</tr><tr><td>
<code>RBesT.MC.rescale</code> </td>
<td> <code>TRUE</code> </td>
<td> Automatic rescaling of raw parameters </td>
</tr><tr><td>
<code>RBesT.verbose</code> </td>
<td> <code>FALSE</code> </td>
<td> requests outputs to be more verbose</td>
</tr><tr><td>
<code>RBesT.integrate_args</code> </td>
<td> <code>list(lower=-Inf,</code> </td>
<td> arguments passed to <code>integrate</code> for</td>
</tr><tr><td>
 </td>
<td> <code>upper=Inf,</code> </td>
<td> intergation of densities</td>
</tr><tr><td>
</td>
<td> <code>rel.tol=.Machine$double.eps^0.25,</code> </td>
<td> </td>
</tr><tr><td>
</td>
<td> <code>abs.tol=.Machine$double.eps^0.25,</code> </td>
<td> </td>
</tr><tr><td>
</td>
<td> <code>subdivisions=1E3)</code> </td>
<td> </td>
</tr></table>

Global Options

See <code>NEWS</code> file.

Option	Default	Description
`RBesT.MC.warmup`	2000	MCMC warmup iterations
`RBesT.MC.iter`	6000	total MCMC iterations
`RBesT.MC.chains`	4	MCMC chains
`RBesT.MC.thin`	4	MCMC thinning
`RBesT.MC.control`	`list(adapt_delta=0.99,`	sets `control` argument for Stan call
	`stepsize=0.01,`
	`max_treedepth=20)`
`RBesT.MC.ncp`	1	parametrization: 0=CP, 1=NCP, 2=Automatic
`RBesT.MC.init`	1	range of initial uniform [-1,1] is the default
`RBesT.MC.rescale`	`TRUE`	Automatic rescaling of raw parameters
`RBesT.verbose`	`FALSE`	requests outputs to be more verbose
`RBesT.integrate_args`	`list(lower=-Inf,`	arguments passed to `integrate` for
	`upper=Inf,`	intergation of densities
	`rel.tol=.Machine$double.eps^0.25,`
	`abs.tol=.Machine$double.eps^0.25,`
	`subdivisions=1E3)`

RBesT-package: R Bayesian Evidence Synthesis Tools

Description

Arguments

Global Options

Version History

Details