BOWLObj-class

Class <code>BOWLObj</code> contains product and sum information required for 
 iteration

internal

Methods to estimate dynamic treatment regimes using Interactive
Q-Learning, Q-Learning, weighted learning, and value-search methods based on
Augmented Inverse Probability Weighted Estimators and Inverse Probability
Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for
Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B.,
Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Shannon Holloway

DynTxRegime

Methods for Estimating Optimal Dynamic Treatment Regimes

BOWLObj-class function

<dl>
<dt><code>prodPi</code></dt>
<dd>Vector of the products of the propensity for the tx received</dd></dl>
<dt><code>sumR</code></dt>
<dd>Vector of the sum of the rewards</dd>
<dt><code>index</code></dt>
<dd>Vector indicating compliance with estimated optimal regime</dd>

Slots

Class BOWLObj — BOWLObj-class

<dl>
<dt><code>prodPi</code></dt>
<dd>Vector of the products of the propensity for the tx received</dd>


<dt><code>sumR</code></dt>
<dd>Vector of the sum of the rewards</dd>


<dt><code>index</code></dt>
<dd>Vector indicating compliance with estimated optimal regime</dd>


</dl>

BOWLObj-class: Class `BOWLObj`

Description

Arguments

Slots