SchurC

<p>Cross information between target and nuisance parameters</p>

<p>Calculates the efficient information \(I_{bb}-I_{ba}I_{aa}^{-1}I_{ab}\).</p>

Implementation of genetic association tests that incorporate the rank-based inverse normal transformation (INT). These tests are broadly indicated for traits with continuous residual distributions. In the presence of non-normal residuals, INT-based tests robustly control the type I error, whereas standard linear regression may not. Moreover, INT-based tests dominate standard linear regression in terms of power. There are two main strategies for incorporating the INT in association analysis. In direct INT (D-INT), the trait is directly transformed. In indirect INT (I-INT), residuals are formed prior to transformation. Neither D-INT nor I-INT is uniformly most powerful. The INT omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach.

SchurC: Schur complement

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