rinvGamma

The random generation of the Inverse gamma distribution

internal

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

rinvGamma function

<dl><dt>n</dt>
<dd>the number of observations</dd>
<dt>a</dt>
<dd>the shape parameter of the inverse gamma distribution</dd>
<dt>b</dt>
<dd>the scale parameter of the inverse gamm distribution</dd></dl>

Arguments

The random generation of the Inverse gamma distribution — rinvGamma

<dl>

<dt>n</dt>
<dd>the number of observations</dd>


<dt>a</dt>
<dd>the shape parameter of the inverse gamma distribution</dd>


<dt>b</dt>
<dd>the scale parameter of the inverse gamm distribution</dd>

</dl>

rinvGamma: The random generation of the Inverse gamma distribution

Description

Usage

Arguments