API Reference#

Kernel Test#

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 ksample kernel-based quadratic distance (KBQD) tests.

Poisson Kernel Test#

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

Class for Poisson kernel-based quadratic distance test 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 is a user interface class that runs a Streamlit dashboard using asyncio.

Datasets#

load_wireless_data([desc, return_X_y, ...])

The wireless data frame has 2000 rows and 8 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.