optimal_k_bin

function that searches for initial parameters of binary outcome function to initiate the sampler

Implementation of the LoTTA (Local Trimmed Taylor Approximation) model
described in "Bayesian Regression Discontinuity Design with Unknown Cutoff"
by Kowalska, van de Wiel, van der Pas (2024) <doi:10.48550/arXiv.2406.11585>.

Julia Kowalska

LoTTA

Bayesian Inference in Regression Discontinuity Designs

optimal_k_bin function

<dl><dt>x</dt>
<dd><ul>
<li>score data</li>
</ul></dd>
<dt>y</dt>
<dd><ul>
<li>outcome data</li>
</ul></dd>
<dt>c</dt>
<dd><ul>
<li>cutoff</li>
</ul></dd>
<dt>lb</dt>
<dd><ul>
<li>minimum window size</li>
</ul></dd>
<dt>ubr</dt>
<dd><ul>
<li>maximum value of the window's right boundary point</li>
</ul></dd>
<dt>ubl</dt>
<dd><ul>
<li>minimum value of the window's left boundary point</li>
</ul></dd></dl>

Arguments

function that searches for initial parameters of binary outcome function to initiate the sampler — optimal_k_bin

<dl>

<dt>x</dt>
<dd><ul>
<li>score data</li>
</ul></dd>


<dt>y</dt>
<dd><ul>
<li>outcome data</li>
</ul></dd>


<dt>c</dt>
<dd><ul>
<li>cutoff</li>
</ul></dd>


<dt>lb</dt>
<dd><ul>
<li>minimum window size</li>
</ul></dd>


<dt>ubr</dt>
<dd><ul>
<li>maximum value of the window's right boundary point</li>
</ul></dd>


<dt>ubl</dt>
<dd><ul>
<li>minimum value of the window's left boundary point</li>
</ul></dd>

</dl>

optimal_k_bin: function that searches for initial parameters of binary outcome function to initiate the sampler

Description

Usage

Value

Arguments