Model and hypothesis | An example | Regularization | Estimation and diagnostics | Precomputed matrices | Reference
Package: RidgeLRGLH 0.1.0
RidgeLRGLH: Ridge-Regularized Largest Root Tests for General Linear Hypotheses
Tests general linear hypotheses in high-dimensional multivariate regression using a ridge-regularized largest root statistic. Estimates the population spectral distribution by linear programming and computes Tracy-Widom centering and scaling constants. Supports fixed, Bayesian, and minimax regularization choices. Implements the methods of Li (2025) <doi:10.48550/arXiv.2504.15510>.
Authors:
RidgeLRGLH_0.1.0.tar.gz
RidgeLRGLH_0.1.0.zip(r-4.7-any)RidgeLRGLH_0.1.0.zip(r-4.6-any)RidgeLRGLH_0.1.0.zip(r-4.5-any)
RidgeLRGLH_0.1.0.tgz(r-4.5-any)
RidgeLRGLH_0.1.0.tar.gz(r-4.7-any)RidgeLRGLH_0.1.0.tar.gz(r-4.6-any)
RidgeLRGLH_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RidgeLRGLH/json (API)
| # Install 'RidgeLRGLH' in R: |
| install.packages('RidgeLRGLH', repos = c('https://haoranli.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/haoranli/ridgelrglh/issues
Last updated from:552a042b9e. Checks:8 OK, 1 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 96 | ||
| source / vignettes | OK | 152 | ||
| linux-release | OK | 85 | ||
| macos-release | FAIL | 47 | ||
| macos-oldrel | OK | 108 | ||
| windows-devel | OK | 73 | ||
| windows-release | OK | 83 | ||
| windows-oldrel | OK | 71 | ||
| wasm-release | OK | 94 |
Exports:estimate_spectrumridge_calibrationridge_glhtridge_root
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimate a Population Covariance Spectrum by Linear Programming | estimate_spectrum |
| Compute Tracy-Widom Centering and Scaling Constants | ridge_calibration |
| Test a General Linear Hypothesis in Multivariate Regression | ridge_glht |
| Ridge-Regularized Largest Root Test for Two Covariance Matrices | ridge_root |