Initial_SHARP_BIN

function that samples initial values for sharp LoTTA model with binary outcomes

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

Initial_SHARP_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 location</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>
<dt>s</dt>
<dd><ul>
<li>seed</li>
</ul></dd></dl>

Arguments

function that samples initial values for sharp LoTTA model with binary outcomes — Initial_SHARP_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 location</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>


<dt>s</dt>
<dd><ul>
<li>seed</li>
</ul></dd>

</dl>

Initial_SHARP_BIN: function that samples initial values for sharp LoTTA model with binary outcomes

Description

Usage

Value

Arguments