__init__(self,
sd=0,
distribution='rdist',
fpp=None,
nbins=400,
**kwargs)
(Constructor)
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L2-Norm the values, convert them to p-values of a given distribution.
WARNING: Highly experimental/slow/etc: no theoretical grounds have been
presented in any paper, nor proven
- Parameters:
sd (int) - Samples dimension (if len(x.shape)>1) on which to operate
distribution (string) - Which distribution to use. Known are: 'rdist' (later normal should
be there as well)
fpp (float) - At what p-value (both tails) if not None, to control for false
positives. It would iteratively prune the tails (tentative real positives)
until empirical p-value becomes less or equal to numerical.
nbins (int) - Number of bins for the iterative pruning of positives
- Overrides:
state.ClassWithCollections.__init__
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