scikits-bootstrap

Scikits.bootstrap provides bootstrap confidence interval algorithms for scipy.

At present, it is rather feature-incomplete and in flux. However, the functions that have been written should be relatively stable as far as results.

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.

In any case, please contact me (Constantine Evans cevans@evanslabs.org) with any questions or suggestions. I'm trying to add documentation, and will be adding tests as well. I'm especially interested, however, in how the API should actually look; please let me know if you think the package should be organized differently.

The package is licensed under the Modified BSD License. It is supported in part by the Evans Foundation.

Version Info

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.