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Getting Started

Installation

Choose the smallest install that fits your workflow.

pip install bootstrapx-lib

Optional extras:

pip install "bootstrapx-lib[pandas]"
pip install "bootstrapx-lib[sklearn]"
pip install "bootstrapx-lib[numba]"
pip install "bootstrapx-lib[pandas,sklearn,numba]"

First bootstrap interval

import numpy as np
from bootstrapx import bootstrap

data = np.random.default_rng(0).normal(loc=5.0, scale=2.0, size=200)
result = bootstrap(data, np.mean, method="bca", n_resamples=4999, random_state=42)

print(result.theta_hat)
print(result.standard_error)
print(result.confidence_interval)

Choosing a method

  • bca: best default for general statistics.
  • percentile: simplest and fast.
  • basic: useful when symmetry assumptions are acceptable.
  • studentized: higher compute cost, useful when bootstrap-t is desired.
  • mbb, stationary, sieve: for dependent time series.
  • cluster, strata: for grouped or sampled data.

Reproducibility

Always pass random_state in production code:

result = bootstrap(data, np.mean, random_state=123)

This is especially important for pipelines, tests, and repeated model evaluation.

Local development

git clone https://github.com/artyerokhin/bootstrapx.git
cd bootstrapx
pip install -e ".[dev]"
pytest tests/ -v