QuadratiK
Implementation in R and Python of a comprehensive set of goodness-of-fit tests and a clustering technique for d-dimensional spherical data d > 1 using kernel-based quadratic distances. it includes: Test for multivariate normality, test for uniformity on the d-dimensional Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson kernel-based density and clustering algorithm for spherical data.
Role: Maintainer
Resources
GitHub repository: QuadratiK on GitHub
CRAN Package: QuadratiK on CRAN
Package site: QuadratiK documantation
Python package: QuadratiK on PyPI
The package is also available as a Dashboard application. Please find the usage instruction here.
robustsur
Robust Estimation for Seemingly Unrelated Regression (SUR) Models.
Role: Author