[](https://doi.org/10.5281/zenodo.3548989)
[](https://codecov.io/gh/cgevans/scikits-bootstrap)
[](https://pypi.org/project/scikits.bootstrap/)
[](https://github.com/cgevans/scikits-bootstrap/releases)
[](https://pypi.org/project/scikits.bootstrap/)
Documentation: [Stable](https://scikits-bootstrap.readthedocs.io/en/stable/), [Latest](https://scikits-bootstrap.readthedocs.io/en/latest/).
scikits-bootstrap
=================
Scikits.bootstrap provides bootstrap statistics confidence interval algorithms
for Numpy/Scipy/Pandas. It originally required scipy, but no longer needs it.
It also provides an algorithm which estimates the probability that the
statistics lies satisfies some criteria, e.g., lies in some interval.
Much of the code has been written based off the descriptions from Efron
and Tibshirani's Introduction to the Bootstrap, and results should match
the results obtained from following those explanations. However, the
current ABC code is based off of the modified-BSD-licensed R port of the
Efron bootstrap code, as I do not believe I currently have a sufficient
understanding of the ABC method to write the code independently.
Please contact me (Constantine Evans <cevans@costinet.org>, or Matrix
<@cge:matrix.org>) with any questions, suggestions, vulnerabilities, or other
comments ([PGP key](https://costinet.org/new-cge-pgp.key)), or, preferably, use
Github's issue and pull requests.
If you'd like to add something, or make improvements, please keep the following
in mind:
- I'd like to keep the library as widely supported as possible, with as
few dependencies as possible, preferably small ones. I'm also very
wary of anything that would break backwards compatibility, even in
unknown ways.
- I am following semantic versioning.
- Code should be black-formatted, and should have type annotations that work
in 3.7 through the latest stable Python, and PyPy3.
- Docstrings should be in Numpy format. They should preferably include
references for the implemented algorithms (see the current code for
examples).
- All code should have working unit tests, preferably ones that do more
than just testing fixed output with a fixed random seed, though given
the non-deterministic nature of the bootstrap, good, deterministic
tests may be difficult. Non-deterministic tests with extremely small
chances of failure are acceptable, but shouldn't use such large numbers
of samples that they overly slow down the tests.
- I am a physicist, and am not very familiar with applications of the
bootstrap for business or development statistics. So if you'd like
to add something for those, it would be useful to give some more
explanation than otherwise as to how they are generally useful.
The package is licensed under the BSD 3-Clause License. It is supported
by the [Evans Foundation](https://evansfmm.org).
Version Information
===================
- v1.1.0: Randomness is now generated via a numpy.random
Generator. Anything that relied on using numpy.random.seed to obtain
deterministic results will fail (mostly of relevance for testing).
Seeds (or Generators) can now be passed to relevant functions with
the `seed` argument, but note that changes in Numpy's random number
generation means this will not give the same results that would be
obtained using `numpy.random.seed` to set the seed in previous
versions.
There is a new pval function, and there are several bugfixes.
Numba is now supported in some instances (np.average or np.mean as
statfunction, 1-D data), using use\_numba=True. Pypy3 is also
supported. Typing information has been added, with code passing
`mypy --strict --allow-untyped-calls --ignore-missing-imports`, and
tests cover 100% of the code (though many tests use fixed seeds).
Handling of multiple data sets (tuples/etc of arrays) now can be
specified as multi="paired" (the previous handling), where the sets
must be of the same length, and samples are taken keeping
corresponding points connected, or multi="independent", treating
data sets as independent and sampling them seperately (in which case
they may be different sizes).
- v1.0.1: Licensing information added.
- v1.0.0: scikits.bootstrap now uses pyerf, which means that it
doesn't actually need scipy at all. It should work with PyPy, has
some improved error and warning messages, and should be a bit faster
in many cases. The old ci\_abc function has been removed: use
method='abc' instead.
- v0.3.3: Bug fixes. Warnings have been cleaned up, and are
implemented for BCa when all statistic values are equal (a common
confusion in prior versions). Related numpy warnings are now
suppressed. Some tests on Python 2 were fixed, and the PyPI website
link is now correct.
- v0.3.2: This version contains various fixes to allow compatibility
with Python 3.3. While I have not used the package extensively with
Python 3, all tests now pass, and importing works properly. The
compatibility changes slightly modify the output of
bootstrap\_indexes, from a Python list to a Numpy array that can be
iterated over in the same manner. This should only be important in
extremely unusual situations.
Installation and Usage
======================
scikits.bootstrap is tested on Python 3.7 - 3.10, and PyPy 3. The package
can be installed using pip.
`pip install scikits.bootstrap`
Usage example for python 3.x:
import scikits.bootstrap as boot
import numpy as np
boot.ci(np.random.rand(100), np.average)
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