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| [](https://doi.org/10.5281/zenodo.3548989) | ||
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| [](https://codecov.io/gh/cgevans/scikits-bootstrap) | ||
| [](https://pypi.org/project/scikits.bootstrap/) | ||
| ] | ||
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| scikits-bootstrap | ||
| ================= | ||
|
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| Scikits.bootstrap provides bootstrap confidence interval algorithms for Numpy/Scipy/Pandas. It originally required scipy, but no longer needs it. | ||
| Scikits.bootstrap provides bootstrap confidence interval algorithms for | ||
| Numpy/Scipy/Pandas. It originally required scipy, but no longer needs | ||
| it. | ||
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| It also provides an algorithm which estimates the probability that the statistics | ||
| lies satisfies some criteria, e.g. lies in some interval. | ||
| It also provides an algorithm which estimates the probability that the | ||
| statistics lies satisfies some criteria, e.g. lies in some interval. | ||
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| 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. | ||
| 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. | ||
| 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. | ||
| 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 BSD 3-Clause License. It is supported in part | ||
| by the Evans Foundation. | ||
| The package is licensed under the BSD 3-Clause License. It is supported | ||
| in part by the Evans Foundation. | ||
|
|
||
| Version Info | ||
| ============ | ||
|
|
||
| - HEAD: 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. | ||
|
|
||
| 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. | ||
|
|
||
| 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. | ||
|
|
||
|
|
||
| - v1.1.0-pre.1: 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. | ||
|
|
||
| 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 3.6 - 3.9, and PyPy 3. The package can be installed using pip. | ||
| scikits.bootstrap is tested on Python 3.6 - 3.9, 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) | ||
| ``` | ||
| import scikits.bootstrap as boot | ||
| import numpy as np | ||
| boot.ci(np.random.rand(100), np.average) |