rinvGamma

internal

Implements a wide range of dose escalation
designs. The focus is on model-based 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. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy
to setup a new design with custom 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.
Further details are presented in
Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.

Daniel Sabanes Bove

crmPack

Object-Oriented Implementation of Dose Escalation Designs

Wai Yin Yeung

Burak Kuersad Guenhan

Giuseppe Palermo

Thomas Jaki

Jiawen Zhu

Ziwei Liao

Dimitris Kontos

Marlene Schulte-Goebel

Doug Kelkhoff

Oliver Boix

Robert Adams

Clara Beck

John Kirkpatrick

Wojciech Wójciak

Guanya Peng

Prerana Chandratre

F. Hoffmann-La Roche AG 

Merck Healthcare KGaA 

Bayer AG 

RPACT GmbH 

rinvGamma function

<dl><dt>n</dt>
<dd>(<code>count</code>) the number of observations.</dd>
<dt>a</dt>
<dd>(<code>number</code>) the shape parameter of the inverse gamma distribution.</dd>
<dt>b</dt>
<dd>(<code>number</code>) the scale parameter of the inverse gamma distribution.</dd></dl>

Arguments

Random Generation for the Inverse Gamma Distribution — rinvGamma

<dl>

<dt>n</dt>
<dd>(<code>count</code>) the number of observations.</dd>


<dt>a</dt>
<dd>(<code>number</code>) the shape parameter of the inverse gamma distribution.</dd>


<dt>b</dt>
<dd>(<code>number</code>) the scale parameter of the inverse gamma distribution.</dd>

</dl>

Random Generation for the Inverse Gamma Distribution

rinvGamma: Random Generation for the Inverse Gamma Distribution

Description

Usage

Value

Arguments