QuadratiK-package {QuadratiK}R Documentation

Collection of Methods Constructed using the Kernel-Based Quadratic Distances

Description

Collection of Methods Constructed using the Kernel-Based Quadratic Distances

QuadratiK provides the first implementation, in R and Python, of a comprehensive set of goodness-of-fit tests and a clustering technique for d-dimensional spherical data d \ge 2 using kernel-based quadratic distances. It includes:

For an introduction to QuadratiK see the vignette Introduction to the QuadratiK Package.

Details

The work has been supported by Kaleida Health Foundation and the National Science Foundation.

Note

The QuadratiK package is also available in Python on PyPI https://pypi.org/project/QuadratiK/ and also as a Dashboard application. Usage instruction for the Dashboard can be found at <https://quadratik.readthedocs.io/en/latest/user_guide/ dashboard_application_usage.html>.

Author(s)

Giovanni Saraceno, Marianthi Markatou, Raktim Mukhopadhyay, Mojgan Golzy

Maintainer: Giovanni Saraceno giovanni.saracen@unipd.it

References

Saraceno, G., Markatou, M., Mukhopadhyay, R. and Golzy, M. (2024). Goodness-of-Fit and Clustering of Spherical Data: the QuadratiK package in R and Python. arXiv preprint arXiv:2402.02290.

Ding, Y., Markatou, M. and Saraceno, G. (2023). “Poisson Kernel-Based Tests for Uniformity on the d-Dimensional Sphere.” Statistica Sinica. doi: doi:10.5705/ss.202022.0347.

Golzy, M. and Markatou, M. (2020) Poisson Kernel-Based Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling, Journal of Computational and Graphical Statistics, 29:4, 758-770, DOI: 10.1080/10618600.2020.1740713.

Markatou, M. and Saraceno, G. (2024). “A Unified Framework for Multivariate Two- and k-Sample Kernel-based Quadratic Distance Goodness-of-Fit Tests.”
https://doi.org/10.48550/arXiv.2407.16374

See Also

Useful links:


[Package QuadratiK version 1.1.3 Index]