ModelParamsNormal-class

.ModelParamsNormal

ModelParamsNormal

.DefaultModelParamsNormal

<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=crmPack&version=2.0.0" alt="[Experimental]"></a>
<code>ModelParamsNormal</code> is the class for a bivariate normal model parameters,
i.e. the mean vector, covariance matrix and precision matrix.
The precision matrix is an inverse of the covariance matrix in the
<code>JAGS</code> and it is computed internally by the object constructor function.

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 

ModelParamsNormal-class function

<dl><dt>mean</dt>
<dd>(<code>numeric</code>) the prior mean vector.</dd>
<dt>cov</dt>
<dd>(<code>matrix</code>) the prior covariance matrix. The precision matrix
<code>prec</code> is internally calculated as an inverse of <code>cov</code>.</dd></dl>

Arguments

<dl>
<dt><code>mean</code></dt>
<dd>(<code>numeric</code>) the mean vector.</dd></dl>
<dt><code>cov</code></dt>
<dd>(<code>matrix</code>) the covariance matrix.</dd>
<dt><code>prec</code></dt>
<dd>(<code>matrix</code>) the precision matrix, which is an inverse matrix of the <code>cov</code>.</dd>

Slots

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
<code>ModelParamsNormal</code> is the class for a bivariate normal model parameters,
i.e. the mean vector, covariance matrix and precision matrix.
The precision matrix is an inverse of the covariance matrix in the
<code>JAGS</code> and it is computed internally by the object constructor function.

ModelParamsNormal — ModelParamsNormal-class

<dl>

<dt>mean</dt>
<dd>(<code>numeric</code>) the prior mean vector.</dd>


<dt>cov</dt>
<dd>(<code>matrix</code>) the prior covariance matrix. The precision matrix
<code>prec</code> is internally calculated as an inverse of <code>cov</code>.</dd>

</dl>

<dl>
<dt><code>mean</code></dt>
<dd>(<code>numeric</code>) the mean vector.</dd>


<dt><code>cov</code></dt>
<dd>(<code>matrix</code>) the covariance matrix.</dd>


<dt><code>prec</code></dt>
<dd>(<code>matrix</code>) the precision matrix, which is an inverse matrix of the <code>cov</code>.</dd>


</dl>

<code>ModelParamsNormal</code>

ModelParamsNormal-class: `ModelParamsNormal`

Description

Usage

Arguments

Slots

See Also

Examples