ReguGLH: General Linear Hypothesis Tests via Spectral Shrinkage

Implements high-dimensional general linear hypothesis tests using ridge, reciprocal cubic polynomial, and user-defined analytic spectral regularizers. Provides prior-based shrinkage selection and Gaussian maximum calibration for composite tests. Implements the methods of Li, Aue and Paul (2020), "High-dimensional general linear hypothesis tests via non-linear spectral shrinkage", Bernoulli, 26(4), 2541-2571 <doi:10.3150/19-BEJ1186>.

Version: 0.1.0
Imports: stats
Published: 2026-10-27
DOI: 10.32614/CRAN.package.ReguGLH
Author: Haoran Li [aut, cre]
Maintainer: Haoran Li <hzl0152 at auburn.edu>
Contact: Haoran Li <haoranli1990@gmail.com> (secondary email)
License: GPL-3
NeedsCompilation: no
Citation: ReguGLH citation info
Materials: README, NEWS
CRAN checks: ReguGLH results

Documentation:

Reference manual: ReguGLH.html , ReguGLH.pdf

Downloads:

Package source: ReguGLH_0.1.0.tar.gz
Windows binaries: r-devel: ReguGLH_0.1.0.zip, r-release: ReguGLH_0.1.0.zip, r-oldrel: not available
macOS binaries: r-release (arm64): ReguGLH_0.1.0.tgz, r-oldrel (arm64): ReguGLH_0.1.0.tgz, r-release (x86_64): ReguGLH_0.1.0.tgz, r-oldrel (x86_64): ReguGLH_0.1.0.tgz

Linking:

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