API Reference

Kernel Tests

KernelTest([h, method, num_iter, b, ...])	Class for performing the kernel-based quadratic distance goodness-of-fit tests using the Gaussian kernel with tuning parameter h.
select_h(x[, y, alternative, method, b, ...])	This function computes the kernel bandwidth of the Gaussian kernel for the one sample, two-sample and k-sample kernel-based quadratic distance (KBQD) tests.

Poisson Kernel Test

PoissonKernelTest(rho[, num_iter, quantile, ...])

Class for Poisson kernel-based quadratic distance tests of Uniformity on the Sphere.

Spherical Clustering

PKBC(num_clust[, max_iter, stopping_rule, ...])	Poisson kernel-based clustering on the sphere.
PKBD()	Class for estimating density and generating samples of Poisson-kernel based distribution (PKBD).

User Interface

UI()	The UI class runs a Streamlit dashboard.

Datasets

load_wireless_data([desc, return_X_y, ...])	The wireless data frame has 2000 rows and 8 columns.
load_wisconsin_breast_cancer_data([desc, ...])	The Wisconsin breast cancer dataset data frame has 569 rows and 31 columns.
load_wine_data([desc, return_X_y, ...])	The wine data frame has 178 rows and 14 columns.

Tools

sample_hypersphere([npoints, ndim, random_state])	Generate random samples from the hypersphere.
stats(x[, y])	The stats function calculates statistics for one or multiple groups of data.
qq_plot(x[, y, dist])	The function qq_plot is used to create a quantile-quantile plot, either for a single sample or for two samples.
sphere3d(x[, y, ...])	The function sphere3d creates a 3D scatter plot with a sphere as the surface and data points plotted on it.
plot_clusters_2d(x[, y])	This function plots a 2D scatter plot of data points, with an optional argument to color the points based on a cluster label, and also plots a unit circle.
spherical_pca(data[, scale])	Perform Spherical Principal Component Analysis (PCA).