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.

D-Variate

dHsic([compute_kernel, bias])

\(d\)-variate Hilbert Schmidt Independence Criterion (dHsic) 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.
SmoothCFTest([num_randfreq])	Smooth Characteristic Function test statistic and p-value
MeanEmbeddingTest([num_randfreq])	Mean Embedding test statistic and p-value.
FriedmanRafsky(**kwargs)	Friedman-Rafksy (FR) test statistic and p-value.
KSampleHHG([compute_distance])	HHG 2-Sample test statistic.

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.
LjungBox([max_lag])	Ljung-Box for Cross Correlation (CorrX) test statistic and p-value.

Discriminability

DiscrimOneSample([is_dist, remove_isolates])	One Sample Discriminability test statistic and p-value.
DiscrimTwoSample([is_dist, remove_isolates])	Two Sample Discriminability test statistic and p-value.

Conditional Independence

ConditionalDcorr([compute_distance, ...])	Conditional Distance Covariance/Correlation (CDcov/CDcorr) test statistic and p-value.
PartialDcorr([compute_distance, use_cov])	Partial Distance Covariance/Correlation (PDcov/PDcorr) test statistic and p-value.
PartialCorr(**kwargs)	Conditional Pearson's correlation test.
FCIT([model, cv_grid, num_perm, prop_test, ...])	Fast Conditional Independence test statistic and p-value
KCI(**kwargs)	Kernel Conditional Independence Test Statistic and P-Value.

Kernel Goodness-of-Fit

FSSD(p, k, V[, null_sim, alpha])

Goodness-of-fit test using The Finite Set Stein Discrepancy statistic.

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.

Conditional Independence Simulations

indep_normal(n[, p, random_state])	Independent standard normal distributions.
indep_lognormal(n[, p, random_state])	Independent lognormal and normal distributions.
indep_binomial(n[, p, random_state])	Independent binomial distributions.
cond_indep_normal(n[, p, random_state])	Conditionally independent normal distributions.
cond_indep_lognormal(n[, p, random_state])	Conditionally independent lognormal and normal distributions.
cond_indep_normal_nonlinear(n[, p, random_state])	Conditionally independent normal distributions.
cond_indep_binomial(n[, p, random_state])	Conditionally independent binomial distributions.
correlated_binomial(n[, p, random_state])	Conditionally dependent binomial distributions.
correlated_normal(n[, p, random_state])	Conditionally dependent normal distributions.
correlated_normal_nonliear(n[, p, random_state])	Conditionally dependent normal distributions with nonlinear dependence.
correlated_lognormal(n[, p, random_state])	Example 5 from [#szekelyPartialDistanceCorrelation2014a]_ \((X, Y, Z) \in \mathbb{R} \times \mathbb{R} \times \mathbb{R}\):
correlated_t_linear(n[, p, random_state])	Conditionally dependent t-distributed data with linear dependence.
correlated_t_quadratic(n[, p, random_state])	Conditionally dependent t-distributed data with quadratic dependence.
correlated_t_nonlinear(n[, p, random_state])	Conditionally dependent t-distributed data with nonlinear dependence.
condi_indep_sim(sim, n, p[, random_state])	Conditional independence simulation generator.

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.
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.multi_compute_kern(*args[, metric, ...])	Kernel similarity matrices for the input matrices.
tools.compute_dist(x, y[, z, metric, workers])	Distance matrices for the inputs.
tools.perm_test(calc_stat, x, y[, z, reps, ...])	Permutation test for the p-value of a nonparametric test.
tools.multi_perm_test(calc_stat, *args[, ...])	Permutation test for the p-value of a nonparametric test with multiple variables.
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 hypothesis tests
ksample.smoothCF.smooth_cf_distance(difference)	Calculates the Smooth CF test statistic using the vector of differences.
ksample.mean_embedding.mean_embed_distance(...)	Calculates the Mean Embedding test statistic using the vector of differences.

Base Classes

independence.base.IndependenceTest([...])	A base class for an independence test.
d_variate.base.DVariateTest([compute_kernel])	A base class for a \(d\)-variate 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.
kgof.base.GofTest(p, alpha)	A base class for a discriminability test.

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