McmcOptions

Initialization function for the "McmcOptions" class

methods

Implements a wide range of model-based dose
escalation designs, ranging from classical and modern continual
reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
dual-endpoint designs taking into account a biomarker/efficacy outcome. The
focus is on Bayesian inference, making it very easy to setup a new design
with its own JAGS code. However, it is also possible to implement 3+3
designs for comparison or models with non-Bayesian estimation. The whole
package is written in a modular form in the S4 class system, making it very
flexible for adaptation to new models, escalation or stopping rules.

Daniel Sabanes Bove

crmPack

Object-Oriented Implementation of CRM Designs

Wai Yin Yeung

Giuseppe Palermo

Thomas Jaki

McmcOptions function

<dl><dt>burnin</dt>
<dd>number of burn-in iterations which are not saved (default:
<code>10,000</code>)</dd>
<dt>step</dt>
<dd>only every step-th iteration is saved after the burn-in
(default: <code>2</code>)</dd>
<dt>samples</dt>
<dd>number of resulting samples (by default <code>10,000</code> will
result)</dd></dl>

Arguments

Initialization function for the "McmcOptions" class — McmcOptions

<dl>

<dt>burnin</dt>
<dd>number of burn-in iterations which are not saved (default:
<code>10,000</code>)</dd>


<dt>step</dt>
<dd>only every step-th iteration is saved after the burn-in
(default: <code>2</code>)</dd>


<dt>samples</dt>
<dd>number of resulting samples (by default <code>10,000</code> will
result)</dd>

</dl>

McmcOptions: Initialization function for the "McmcOptions" class

Description

Usage

Value

Arguments