A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics. Also provides stochastic singular value decomposition for dense or sparse matrices.
The DESCRIPTION file:
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This package computes tail probabilities for large quadratic forms, with the motivation being the SKAT test used in DNA sequence association studies. The main function is
pQF, but also see
SKAT.matrixfree for fast use with sparse genotypes.
Maintainer: Thomas Lumley <[email protected]>
Lumley et al. (forthcoming) Sequence kernel association tests for large sets of markers: tail probabilities for large quadratic forms
Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp (2010) Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. http://arxiv.org/abs/0909.4061.
Lee, S., with contributions from Larisa Miropolsky, and Wu, M. (2015). SKAT: SNP-Set (Sequence) Kernel Association Test. R package version 1.1.2.
Lee, S., Wu, M. C., Cai, T., Li, Y., Boehnke, M., and Lin, X. (2011). Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics, 89:82-93.
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