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

fit function

<dl><dt>stan_data</dt>
<dd>A named list providing the data for the Stan model.</dd>
<dt>model</dt>
<dd>A character string specifying the model type.
Must be either "blm" (Bayesian linear model) or "bnb" (Bayesian negative binomial model).
Defaults to "blm".</dd>
<dt>...</dt>
<dd>Additional options to be passed through to <code>rstan::sampling</code>.</dd></dl>

Arguments

Fits Stan model. — fit

<dl>

<dt>stan_data</dt>
<dd>A named list providing the data for the Stan model.</dd>


<dt>model</dt>
<dd>A character string specifying the model type.
Must be either "blm" (Bayesian linear model) or "bnb" (Bayesian negative binomial model).
Defaults to "blm".</dd>


<dt>...</dt>
<dd>Additional options to be passed through to <code>rstan::sampling</code>.</dd>

</dl>

fit: Fits Stan model.

Description

Usage

Value

Arguments