PointMassPrior-class

PointMassPrior

<code>PointMassPrior</code> is a sub-class of <code>Prior</code>
representing a univariate prior over a discrete set of points with positive
probability mass.

Optimize one or two-arm, two-stage designs for clinical trials with
respect to several implemented objective criteria or custom objectives.
Optimization under uncertainty and conditional (given stage-one outcome)
constraints are supported.
See Pilz et al. (2019) <doi:10.1002/sim.8291> and
Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

Maximilian Pilz

adoptr

Adaptive Optimal Two-Stage Designs

Kevin Kunzmann

Jan Meis

Nico Bruder

PointMassPrior-class function

<dl><dt>theta</dt>
<dd>numeric vector of pivot points with positive prior mass</dd>
<dt>mass</dt>
<dd>numeric vector of probability masses at the pivot points
(must sum to 1)</dd>
<dt>label</dt>
<dd>object label (string)</dd></dl>

Arguments

<dl>
<dt><code>theta</code></dt>
<dd>cf. parameter 'theta'</dd></dl>
<dt><code>mass</code></dt>
<dd>cf. parameter 'mass'</dd>

Slots

Univariate discrete point mass priors — PointMassPrior-class

<dl>

<dt>theta</dt>
<dd>numeric vector of pivot points with positive prior mass</dd>


<dt>mass</dt>
<dd>numeric vector of probability masses at the pivot points
(must sum to 1)</dd>


<dt>label</dt>
<dd>object label (string)</dd>

</dl>

<dl>
<dt><code>theta</code></dt>
<dd>cf. parameter 'theta'</dd>


<dt><code>mass</code></dt>
<dd>cf. parameter 'mass'</dd>


</dl>

PointMassPrior-class: Univariate discrete point mass priors

Description

Usage

Value

Arguments

Slots

See Also

Examples