Multivariate Pattern Analysis in Python

# mvpa2.clfs.stats

Estimator for classifier error distributions.

Functions

`auto_null_dist`(dist) | Cheater for human beings – wraps `dist` if needed with some
`match_distribution`(data[, nsamples, loc, …]) | Determine best matching distribution.
`nanmean`(x[, axis]) | Compute the mean over the given axis ignoring NaNs.
`plot_distribution_matches`(data, matches[, …]) | Plot best matching distributions

Classes

`AdaptiveNormal`(dist, **kwargs) | Adaptive Normal Distribution: params are (0, sqrt(1/nfeatures))
`AdaptiveNullDist`(dist, **kwargs) | Adaptive distribution which adjusts parameters according to the data
`AdaptiveRDist`(dist, **kwargs) | Adaptive rdist: params are (nfeatures-1, 0, 1)
`FixedNullDist`(dist, **kwargs) | Proxy/Adaptor class for SciPy distributions.
`MCNullDist`(permutator[, dist_class, measure]) | Null-hypothesis distribution is estimated from randomly permuted data labels.
`Nonparametric`(dist_samples[, correction]) | Non-parametric 1d distribution – derives cdf based on stored values.
`NullDist`([tail]) | Base class for null-hypothesis testing.
`rv_semifrozen`(dist[, loc, scale, args]) | Helper proxy-class to fit distribution when some parameters are known