Package: cxreg 1.1.4

Younghoon Kim

cxreg: Complex-Valued Lasso and Complex-Valued Graphical Lasso

Implements 'glmnet'-style complex-valued lasso (CLASSO) and complex-valued graphical lasso (CGLASSO) via a pathwise coordinate descent algorithm for complex-valued parameters, using an isomorphism between complex numbers and 2x2 orthogonal matrices. Also provides a full inference pipeline for high-dimensional sparse spectral precision matrices, including data-driven bandwidth selection, one-step debiasing, asymptotic variance estimation, entry-wise confidence regions, and FDR-controlled hypothesis testing. Supporting tools for cross-validation, simulation, coefficient extraction, and plotting are included. See Deb, Kuceyeski, and Basu (2024) <doi:10.48550/arXiv.2401.11128> and Deb, Kim, and Basu (2026) <doi:10.48550/arXiv.2606.07986>.

Authors:Younghoon Kim [aut, cre], Navonil Deb [aut], Sumanta Basu [aut]

cxreg_1.1.4.tar.gz
cxreg_1.1.4.zip(r-4.7-x86_64)cxreg_1.1.4.zip(r-4.7-arm64)cxreg_1.1.4.zip(r-4.6-x86_64)cxreg_1.1.4.zip(r-4.6-arm64)
cxreg_1.1.4.tar.gz(r-4.7-arm64)cxreg_1.1.4.tar.gz(r-4.7-x86_64)cxreg_1.1.4.tar.gz(r-4.6-arm64)cxreg_1.1.4.tar.gz(r-4.6-x86_64)
cxreg_1.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cxreg/json (API)

# Install 'cxreg' in R:
install.packages('cxreg', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.30 score 8 scripts 281 downloads 17 exports 15 dependencies

Last updated from:159d01d99f. Checks:8 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING172
linux-devel-x86_64WARNING183
source / vignettesOK242
linux-release-arm64WARNING167
linux-release-x86_64WARNING176
windows-devel-arm64WARNING226
windows-devel-x86_64WARNING163
windows-release-arm64WARNING205
windows-release-x86_64WARNING146
wasm-releaseOK122

Exports:buildPredmatcglassoclassoclasso.controlcoef.classocv.classodecglassodft.alldft.jfhat_atpredict.classoprint.classoprint.cv.classoselect_mspec.fdrspec.testvar.cov

Dependencies:codetoolsdotCall64fieldsforeachgdatagtoolsiteratorslatticemapsMatrixmvtnormRColorBrewerRcppspamviridisLite

Inference for Sparse Spectral Precision Matrices with cxreg
Introduction | Background | Spectral density and the DFT | The CGLASSO and deCGLASSO estimators | Step 1 — Generate a multivariate white noise process | Step 2 — Bandwidth selection via GCV | Step 3 — Compute the DFT and smoothed periodogram | Step 4 — Fit CGLASSO and compute the deCGLASSO estimator | Step 5 — Estimate the asymptotic variance | Step 6 — Entry-wise tests and confidence regions | Step 7 — FDR-controlled support recovery | Summary of functions | References
Rendered fromcxreg-inference.Rmdusingknitr::rmarkdown

Last update: 2026-07-11
Started: 2026-07-11

An Introduction to cxreg
Introduction | Installation | Example: classo | Example: cglasso | Example: Sparse Spectral Precision Matrix Estimation | Step 1 — Generate a multivariate Gaussian white noise process: | Step 2 — Compute the DFT and smoothed periodogram: | Step 3 — Estimate sparse spectral precision matrix via \texttt{cglasso}: | Step 4 — Visualize the estimated spectral precision matrix: | Step 5 — Compare to the true precision matrix:
Rendered fromcxreg.Rmdusingknitr::rmarkdown

Last update: 2026-07-05
Started: 2026-07-05

Readme and manuals

Help Manual

Help pageTopics
Complex-valued Lasso, graphical Lasso, and spectral inferencecxreg-package
Build Prediction MatrixbuildPredmat buildPredmat.default
fit a complex-valued graphical lassocglasso
Example data for cglassocglasso_example
fit a complex-valued graphical Lasso for a path of lambda valuescglasso.path
fit a complex-valued lassoclasso
Example data for classoclasso_example
Internal classo parametersclasso.control
fit a complex-valued lasso for a path of lambda valuesclasso.path
Extract coefficients from a classo objectcoef.classo predict.classo
Extract coefficients from a cv.classo objectcoef.cv.classo
Cross-validation for classocv.classo
Simulated data for the cxreg vignettecxreg x y
Debias the cglasso estimatordecglasso
Discrete Fourier Transform of matrix Xdft.all
Discrete Fourier Transform of matrix X at a fixed frequency omega_jdft.j
Extract the family from a classo objectfamily.classo family.classofit family.cv.classo
Smoothed periodogram at a fixed frequencyfhat_at
Plot heatmap from a "cglasso" objectplot.cglasso
plot coefficients from a "classo" objectplot.classo
plot the cross-validation curve produced by cv.classoplot.cv.classo
Make predictions from a "cv.classo" objectpredict.cv.classo
print a classo objectprint.classo
print a cross-validated classo objectprint.cv.classo
Select the optimal bandwidth 'm' for spectral density estimationselect_m
FDR-controlled hypothesis testing for spectral precision matrix entriesspec.fdr
Confidence regions and test statistics for spectral precision matrix entriesspec.test
Variance-covariance estimation for the debiased spectral precision matrixvar.cov