API Reference

Independence

MaxMargin(indep_test[, compute_distkern, bias])	Maximal Margin test statistic and p-value.
KMERF([forest, ntrees])	Kernel Mean Embedding Random Forest (KMERF) test statistic and p-value.
MGC([compute_distance])	Multiscale Graph Correlation (MGC) test statistic and p-value.
Dcorr([compute_distance, bias])	Distance Correlation (Dcorr) test statistic and p-value.
Hsic([compute_kernel, bias])	Hilbert Schmidt Independence Criterion (Hsic) test statistic and p-value.
HHG([compute_distance])	Heller Heller Gorfine (HHG) test statistic and p-value.
CCA()	Cannonical Correlation Analysis (CCA) test statistic and p-value.
RV()	Rank Value (RV) test statistic and p-value.

K-Sample

KSample(indep_test[, compute_distkern, bias])	Nonparametric K-Sample Testing test statistic and p-value.
Energy([compute_distance, bias])	Energy test statistic and p-value.
MMD([compute_kernel, bias])	Maximum Mean Discrepency (MMD) test statistic and p-value.
DISCO([compute_distance, bias])	Distance Components (DISCO) test statistic and p-value.
MANOVA()	Multivariate analysis of variance (MANOVA) test statistic and p-value.
Hotelling()	Hotelling \(T^2\) test statistic and p-value.

Time-Series

MGCX([compute_distance, max_lag])	Cross Multiscale Graph Correlation (MGCX) test statistic and p-value.
DcorrX([compute_distance, max_lag])	Cross Distance Correlation (DcorrX) test statistic and p-value.

Discriminability

DiscrimOneSample([is_dist, remove_isolates])	1 Sample Discriminability test statistic and p-value.
DiscrimTwoSample([is_dist, remove_isolates])	A class that compares the discriminability of two datasets.

Simulations

Independence Simulations

linear(n, p[, noise, low, high])	Linear simulation.
exponential(n, p[, noise, low, high])	Exponential simulation.
cubic(n, p[, noise, low, high, cubs, scale])	Cubic simulation.
joint_normal(n, p[, noise])	Joint Normal simulation.
step(n, p[, noise, low, high])	Step simulation.
quadratic(n, p[, noise, low, high])	Quadratic simulation.
w_shaped(n, p[, noise, low, high])	W-Shaped simulation.
spiral(n, p[, noise, low, high])	Spiral simulation.
uncorrelated_bernoulli(n, p[, noise, prob])	Uncorrelated Bernoulli simulation.
logarithmic(n, p[, noise])	Logarithmic simulation.
fourth_root(n, p[, noise, low, high])	Fourth Root simulation.
sin_four_pi(n, p[, noise, low, high])	Sine 4\(\pi\) simulation.
sin_sixteen_pi(n, p[, noise, low, high])	Sine 16\(\pi\) simulation.
square(n, p[, noise, low, high])	Square simulation.
two_parabolas(n, p[, noise, low, high, prob])	Two Parabolas simulation.
circle(n, p[, noise, low, high])	Circle simulation.
ellipse(n, p[, noise, low, high])	Ellipse simulation.
diamond(n, p[, noise, low, high])	Diamond simulation.
multiplicative_noise(n, p)	Multiplicative Noise simulation.
multimodal_independence(n, p[, prob, sep1, sep2])	Multimodal Independence data.
indep_sim(sim, n, p, **kwargs)	Independence simulation generator.

K-Sample Simulations

rot_ksamp(sim, n, p[, k, noise, degree, ...])	Rotates input simulations to produce a k-sample simulation.
gaussian_3samp(n[, epsilon, weight, case])	Generates 3 sample of gaussians corresponding to 5 cases.

Time-Series Simulations

indep_ar(n[, lag, phi, sigma])	2 independent, stationary, autoregressive time series simulation.
cross_corr_ar(n[, lag, phi, sigma])	2 linearly dependent time series simulation.
nonlinear_process(n[, lag, phi, sigma])	2 nonlinearly dependent time series simulation.
ts_sim(sim, n, **kwargs)	Time-series simulation generator.

Miscellaneous

independence.sim_matrix(model, x)	Computes the similarity matrix from a random forest.
independence.INDEP_TESTS	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
ksample.k_sample_transform(inputs[, test_type])	Computes a k-sample transform of the inputs.
tools.compute_kern(x, y[, metric, workers])	Kernel similarity matrices for the inputs.
tools.compute_dist(x, y[, metric, workers])	Distance matrices for the inputs.
tools.perm_test(calc_stat, x, y[, reps, ...])	Permutation test for the p-value of a nonparametric test.
tools.chi2_approx(calc_stat, x, y)	Fast chi-squared approximation for the p-value.
tools.power(test, sim_type[, sim, n, alpha, ...])	Computes empircal power for k-sample tests

Base Classes

independence.base.IndependenceTest([...])	A base class for an independence test.
ksample.base.KSampleTest([compute_distance, ...])	A base class for a k-sample test.
time_series.base.TimeSeriesTest([...])	A base class for a time-series test.
discrim.base.DiscriminabilityTest()	A base class for a discriminability test.