Impact Measurement Toolkit
Create a Baseline Balance Plot.
Bayesian Linear Model Factory
Calculate Probability of Posterior Draws Falling Within a Range
Calculate Effect Sizes for Treatment vs. Control
Check Baseline Equivalency.
Cleans and prepares data for analysis
Count missing values (NA) in a dataframe
Cox's Proportional Hazards Index (Cox's C)
Converts a dataframe into a named list to provide data to a Stan model
Calculate credible interval from MCMC draws
Combine and Unite Columns
Add a Random Treatment Indicator Column to a Data Frame
Fits Stan model.
Extracts parameter from Stan model.
Hedges' g Effect Size with Pooled Standard Deviation
The 'imt' package
Bayesian Logit Model Factory
Calculate logit link and sample from binomial distribution
MCMC Checks
Create a Meta-Analysis Object Using Data From Previous Studies
Bayesian Negative Binomial Model Factory
Pipe operator
Calculate Point Estimate (Median or Mean) as Percentage
Randomly Assign Treatment While Controlling for Baseline Equivalency
Randomization Class for Treatment Assignment
Validate a Logical Subgroup Vector
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>.