HHG (version 1.4)

HHG-package: Heller-Heller-Gorfine Tests of Independence

Description

Heller-Heller-Gorfine (HHG) tests are a set of powerful statistical tests of independnece between two random vectors of arbitrary dimensions, given a finite sample. For testing independence between two scalar random variables, the package also contains implementations of the data-derived partitions (DDP) and all-data-partitions (ADP) tests, which are distribution-free and thus much faster to apply.

Details

ll{ Package: HHG Type: Package Version: 1.4 Date: 2014-05-05 License: GPL-2 } The package contains two major functions: hhg.test, which implements the HHG multivariate independence test described in Heller et al. (2013), and xdp.test, which implements the DDP and ADP univariate independence tests from Heller et al. (2014). In addition, in order to demonstrate the tests, the function hhg.example.datagen generates simulated data, and the dataset hughes contains some gene expression data.

References

Heller, R., Heller, Y., & Gorfine, M. (2013). A consistent multivariate test of association based on ranks of distances. Biometrika, 100(2), 503-510. Heller, R., Heller, Y., Kaufman S., & Gorfine, M. (2014). Consistent distribution-free tests of association between univariate random variables. arXiv:1308.1559.

Examples

# See examples in the documentation for hhg.test and xdp.test.