1.1.0

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).

Repository main branch is now named main.

1.1.0-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).

1.0.1

This version adds proper licensing information.

1.0.0

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.

0.3.3

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.

scikits-bootstrap-0.3.2

oops, add Python 3 to setup.py for PyPI category inclusion.