bootstrapped
Implementations of the percentile based bootstrap
Decision gist · record as of 2026-08-14
No. The package is abandoned (last release 2018-10-17, repository archived) with no maintenance or security updates. High install friction from source-only distribution. License terms are unclear in metadata. Unless you are working with legacy code already using this package, choose an actively maintained alternative for bootstrap statistics.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires external dependencies (numpy, and matplotlib/pandas for power analysis).
- No Python version constraint specified in metadata.
- High install friction due to source-only distribution.
License · maintenance · safety
(unclear) — License treatment is unclear; the description mentions BSD licensing and patent grant, but the metadata does not specify SPDX or raw license text. Verify actual license terms before use.
last release 2018-10-17 (2858 days) · last repo commit 2019-11-11 · 637 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 87,955 downloads/mo, #13,760 on PyPI
Alternatives
Verify before relying
import bootstrapped.bootstrap as bs
import bootstrapped.stats_functions as bs_stats
samples = [1, 2, 3, 4, 5]
print(bs.bootstrap(samples, stat_func=bs_stats.mean))- Whether the package works with current Python and dependency versions (last tested in 2018)
- Exact license terms and compatibility (BSD + patent grant specifics)
- Whether multithreading and sparse array support mentioned in description are production-ready
What it is and what it does
bootstrapped is a statistical library that computes confidence intervals by resampling data with replacement and analyzing the resulting distributions. It implements pivotal and percentile-based bootstrap methods to estimate error bars and bounds on population statistics from sample data. The library is designed for A/B testing and hypothesis validation, where you need to quantify uncertainty around observed metrics like conversion rates or mean values.
The package provides functions to calculate confidence intervals for single populations and comparative tests, plus statistical power analysis. It supports multithreaded computation and both dense and sparse arrays. However, the project has been abandoned since 2018-10-17, with no updates to handle modern Python or dependency versions, and license terms are not clearly specified in the package metadata.
Use it for
- Compute error bars and confidence bounds for A/B test results to quantify improvement uncertainty
- Estimate population statistics from sample data with confidence intervals
- Perform statistical power analysis to determine required sample sizes for experiments
- Validate hypothesis test results by bootstrapping to understand sampling variation
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The package is abandoned (last release 2018-10-17, repository archived) with no maintenance or security updates. High install friction from source-only distribution. License terms are unclear in metadata. Unless you are working with legacy code already using this package, choose an actively maintained alternative for bootstrap statistics.
Install
bootstrapped on PyPI
Before you install
High install friction due to source-only distribution. Package is abandoned—last release was 2018-10-17 and repository was archived. No active maintenance or security updates.
Requires external dependencies (numpy, and matplotlib/pandas for power analysis). No Python version constraint specified in metadata.
License in practice
License treatment is unclear; the description mentions BSD licensing and patent grant, but the metadata does not specify SPDX or raw license text. Verify actual license terms before use.
Quickstart
import bootstrapped.bootstrap as bs
import bootstrapped.stats_functions as bs_stats
samples = [1, 2, 3, 4, 5]
print(bs.bootstrap(samples, stat_func=bs_stats.mean))Verify before relying
- Whether the package works with current Python and dependency versions (last tested in 2018)
- Exact license terms and compatibility (BSD + patent grant specifics)
- Whether multithreading and sparse array support mentioned in description are production-ready
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,858 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 87,955 / month, #13,760 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: bootstrapped-0.0.2.tar.gz
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See also arch · forestci · pingouin · piecewise-regression · imbalanced-learn · diptest · pyperf · powershap · rdrobust · ngboost