oaxaca_blinder 0.2.2

A Rust library for performing Oaxaca-Blinder decomposition on Polars DataFrames, with support for categorical variables and bootstrapped standard errors.
Documentation 
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use oaxaca_blinder::OaxacaBuilder;
use polars::prelude::*;
use serde_json::Value;

#[test]
fn test_export_methods() -> Result<(), Box<dyn std::error::Error>> {
    let df = df!(
        "wage" => &[10.0, 12.0, 11.0, 13.0, 15.0, 20.0, 22.0, 21.0, 23.0, 25.0],
        "education" => &[12.0, 16.0, 14.0, 16.0, 18.0, 12.0, 16.0, 14.0, 16.0, 18.0],
        "gender" => &["F", "F", "F", "F", "F", "M", "M", "M", "M", "M"]
    )?;

    let results = OaxacaBuilder::new(df, "wage", "gender", "F")
        .predictors(&["education"])
        .bootstrap_reps(10) // Low reps for speed
        .run()?;

    // Test LaTeX
    let latex = results.to_latex();
    assert!(latex.contains("\\begin{table}"));
    assert!(latex.contains("explained"));
    assert!(latex.contains("unexplained"));

    // Test Markdown
    let markdown = results.to_markdown();
    assert!(markdown.contains("| Component | Estimate |"));
    assert!(markdown.contains("| explained |"));

    // Test JSON
    let json_str = results.to_json()?;
    let json: Value = serde_json::from_str(&json_str)?;

    assert!(json.get("total_gap").is_some());
    assert!(json.get("two_fold").is_some());
    assert!(json["two_fold"].get("aggregate").is_some());

    Ok(())
}