Booted

Booted is a fast, flexible Rust crate for bootstrapping estimators on arbitrary data structures.

It makes no assumptions about the shape or type of your data. Instead, it operates on data indices, allowing you to bootstrap scalars, vectors, or complex nested structures seamlessly. Powered by rayon, Booted automatically parallelizes the resampling and estimation process for high performance.

Features

• Agnostic to data shape: Bootstrap scalars, arrays, or custom structs.
• Multiple Sampling Strategies: Supports standard Simple (n-out-of-n), m-out-of-n, and Block bootstrapping.
• Bias Correction: Easily wrap estimators to perform double-bootstrap bias correction.
• Parallel by Default: Heavily leverages rayon to compute samples across all available CPU cores.
• Ergonomic API: Built using the bon builder pattern for clean, readable configuration.

Quick Start

Here is a complete example of calculating the bootstrap mean, standard error, and 95% confidence interval for a simple 1D dataset:

use booted::bootstrap::{Bootstrap, Estimator};
use booted::summary::{BootstrapSummary, Summarizable};

fn main() {
    // 1. Your arbitrary data
    let data = vec![1.2, 2.3, 1.9, 2.5, 2.1, 3.0, 2.2, 1.8];
    let n = data.len();

    // 2. Configure the Estimator
    // The estimator closure receives a subset of indices and returns your calculated statistic.
    let estimator = Estimator::new()
        .indices((0..n).collect())
        .from(move |indices: &[usize]| {
            let sum: f64 = indices.iter().map(|&i| data[i]).sum();
            Some(sum / indices.len() as f64)
        })
        .build();

    // 3. Configure and run the Bootstrap
    let result = Bootstrap::new()
        .estimator(estimator)
        .n_boot(5000) // Generate 5000 resamples
        .build()
        .run();

    // 4. Summarize the results
    let summary: BootstrapSummary<f64> = result.summarize();

    println!("Bootstrap Mean: {:.4}", summary.statistics.mean);
    println!("Standard Error (StdDev): {:.4}", summary.statistics.stddev);
    println!(
        "95% Confidence Interval: [{:.4}, {:.4}]",
        summary.statistics.ci_95.low, summary.statistics.ci_95.high
    );
}