cleanData

This function performs a series of data cleaning and preprocessing steps
to ensure the data is suitable for analysis. This includes:<ul>
<li>Missing data handling</li>
<li>Variable type checks</li>
<li>Collinearity and zero-variance feature removal</li>
</ul>

A toolkit for causal inference in experimental and observational
studies. Implements various simple Bayesian models including linear,
negative binomial, and logistic regression for impact estimation.
Provides functionality for randomization and checking baseline equivalence
in experimental designs. The package aims to simplify the process of
impact measurement for researchers and analysts across different fields.
Examples and detailed usage instructions are available at
<https://book.martinez.fyi>.

Ignacio Martinez

Impact Measurement Toolkit

cleanData function

<dl><dt>data</dt>
<dd>A data.frame containing the data to be cleaned.</dd>
<dt>y</dt>
<dd>Name of the dependent variable (character).</dd>
<dt>treatment</dt>
<dd>Name of the treatment variable (character, should be logical).</dd>
<dt>x</dt>
<dd>Names of the covariates to include in the model (character vector, optional).</dd>
<dt>binary</dt>
<dd>Should the dependent variable be treated as binary? Default is FALSE</dd></dl>

Arguments

Cleans and prepares data for analysis — cleanData

<dl>

<dt>data</dt>
<dd>A data.frame containing the data to be cleaned.</dd>


<dt>y</dt>
<dd>Name of the dependent variable (character).</dd>


<dt>treatment</dt>
<dd>Name of the treatment variable (character, should be logical).</dd>


<dt>x</dt>
<dd>Names of the covariates to include in the model (character vector, optional).</dd>


<dt>binary</dt>
<dd>Should the dependent variable be treated as binary? Default is FALSE</dd>

</dl>

cleanData: Cleans and prepares data for analysis

Description

Usage

Value

Arguments