{"id":23082615,"url":"https://github.com/philboileau/cvcovest","last_synced_at":"2026-03-08T13:36:18.325Z","repository":{"id":40551711,"uuid":"256326827","full_name":"PhilBoileau/cvCovEst","owner":"PhilBoileau","description":"An R package for assumption-lean covariance matrix estimation in high dimensions","archived":false,"fork":false,"pushed_at":"2024-02-17T20:10:48.000Z","size":5115,"stargazers_count":13,"open_issues_count":3,"forks_count":4,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-11-28T18:09:44.853Z","etag":null,"topics":["covariance-matrix-estimation","cross-validation","high-dimensional-statistics","nonparametric-statistics"],"latest_commit_sha":null,"homepage":"https://philboileau.github.io/cvCovEst/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PhilBoileau.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2020-04-16T20:50:11.000Z","updated_at":"2024-08-08T06:38:56.000Z","dependencies_parsed_at":"2024-02-17T20:47:00.133Z","dependency_job_id":null,"html_url":"https://github.com/PhilBoileau/cvCovEst","commit_stats":{"total_commits":410,"total_committers":10,"mean_commits":41.0,"dds":"0.37073170731707317","last_synced_commit":"89d57c6326f3723dca9e8e878f1d09c18b85cb51"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhilBoileau%2FcvCovEst","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhilBoileau%2FcvCovEst/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhilBoileau%2FcvCovEst/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhilBoileau%2FcvCovEst/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PhilBoileau","download_url":"https://codeload.github.com/PhilBoileau/cvCovEst/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":229982055,"owners_count":18154511,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["covariance-matrix-estimation","cross-validation","high-dimensional-statistics","nonparametric-statistics"],"created_at":"2024-12-16T14:56:05.172Z","updated_at":"2026-03-08T13:36:17.826Z","avatar_url":"https://github.com/PhilBoileau.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nbibliography: \"inst/REFERENCES.bib\"\n---\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# R/`cvCovEst`\n\n\u003c!-- badges: start --\u003e\n[![CircleCI](https://dl.circleci.com/status-badge/img/gh/PhilBoileau/cvCovEst/tree/master.svg?style=svg)](https://app.circleci.com/pipelines/github/PhilBoileau/cvCovEst?branch=master)\n[![codecov](https://codecov.io/gh/PhilBoileau/cvCovEst/branch/master/graph/badge.svg?token=miHiqpGXxJ)](https://app.codecov.io/gh/PhilBoileau/cvCovEst)\n[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.03273/status.svg)](https://doi.org/10.21105/joss.03273)\n[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](https://opensource.org/license/mit/)\n\u003c!-- badges: end --\u003e\n\n\u003e Cross-Validated Covariance Matrix Estimation\n\n__Authors:__ [Philippe Boileau](https://pboileau.ca),\n[Brian Collica](https://www.linkedin.com/in/brian-collica-553b0b94), and\n[Nima Hejazi](https://nimahejazi.org)\n\n---\n\n## What's `cvCovEst`?\n\n`cvCovEst` implements an efficient cross-validated procedure for covariance\nmatrix estimation, particularly useful in high-dimensional settings. The\ngeneral methodology allows for cross-validation to be used to data adaptively\nidentify the optimal estimator of the covariance matrix from a prespecified set\nof candidate estimators. An overview of the framework is provided in the\npackage vignette. For a more detailed description, see @boileau2021. A suite of\nplotting and diagnostic tools are also included.\n\n---\n\n## Installation\n\nFor standard use, install `cvCovEst` from\n[CRAN](https://cran.r-project.org/package=cvCovEst):\n\n```{r CRAN-install, eval=FALSE}\ninstall.packages(\"cvCovEst\")\n```\n\nThe _development version_ of the package may be installed from GitHub using\n[`remotes`](https://CRAN.R-project.org/package=remotes):\n\n```{r gh-master-installation, eval=FALSE}\nremotes::install_github(\"PhilBoileau/cvCovEst\")\n```\n\n---\n\n## Example\n\nTo illustrate how `cvCovEst` may be used to select an optimal covariance matrix\nestimator via cross-validation, consider the following toy example:\n\n```{r example, message=FALSE, warning=FALSE}\nlibrary(MASS)\nlibrary(cvCovEst)\nset.seed(1584)\n\n# generate a 50x50 covariance matrix with unit variances and off-diagonal\n# elements equal to 0.5\nSigma \u003c- matrix(0.5, nrow = 50, ncol = 50) + diag(0.5, nrow = 50)\n\n# sample 50 observations from multivariate normal with mean = 0, var = Sigma\ndat \u003c- mvrnorm(n = 50, mu = rep(0, 50), Sigma = Sigma)\n\n# run CV-selector\ncv_cov_est_out \u003c- cvCovEst(\n    dat = dat,\n    estimators = c(linearShrinkLWEst, denseLinearShrinkEst,\n                   thresholdingEst, poetEst, sampleCovEst),\n    estimator_params = list(\n      thresholdingEst = list(gamma = c(0.2, 2)),\n      poetEst = list(lambda = c(0.1, 0.2), k = c(1L, 2L))\n    ),\n    cv_loss = cvMatrixFrobeniusLoss,\n    cv_scheme = \"v_fold\",\n    v_folds = 5\n  )\n\n# print the table of risk estimates\n# NOTE: the estimated covariance matrix is accessible via the `$estimate` slot\ncv_cov_est_out$risk_df\n```\n\n---\n\n## Issues\n\nIf you encounter any bugs or have any specific feature requests, please [file\nan issue](https://github.com/PhilBoileau/cvCovEst/issues).\n\n---\n\n## Contributions\n\nContributions are very welcome. Interested contributors should consult our\n[contribution\nguidelines](https://github.com/PhilBoileau/cvCovEst/blob/master/CONTRIBUTING.md)\nprior to submitting a pull request.\n\n---\n\n## Citation\n\nPlease cite the following paper when using the `cvCovEst` R software package.\n\n```\n@article{cvCovEst2021,\n  doi = {10.21105/joss.03273},\n  url = {https://doi.org/10.21105/joss.03273},\n  year = {2021},\n  publisher = {The Open Journal},\n  volume = {6},\n  number = {63},\n  pages = {3273},\n  author = {Philippe Boileau and Nima S. Hejazi and Brian Collica and Mark J. van der Laan and Sandrine Dudoit},\n  title = {cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in `R`},\n  journal = {Journal of Open Source Software}\n}\n\n```\n\nWhen describing or discussing the theory underlying the `cvCovEst` method, or\nsimply using the method, please cite the pre-print below.\n\n```\n@article{boileau2022,\n\tauthor = {Philippe Boileau and Nima S. Hejazi and Mark J. van der Laan and Sandrine Dudoit},\n\tdoi = {10.1080/10618600.2022.2110883},\n\teprint = {https://doi.org/10.1080/10618600.2022.2110883},\n\tjournal = {Journal of Computational and Graphical Statistics},\n\tnumber = {ja},\n\tpages = {1-28},\n\tpublisher = {Taylor \u0026 Francis},\n\ttitle = {Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions},\n\turl = {https://doi.org/10.1080/10618600.2022.2110883},\n\tvolume = {0},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1080/10618600.2022.2110883}}\n\n```\n\n---\n\n## License\n\n\u0026copy; 2020-2023 [Philippe Boileau](https://pboileau.ca)\n\nThe contents of this repository are distributed under the MIT license. See file\n[`LICENSE.md`](https://github.com/PhilBoileau/cvCovEst/blob/master/LICENSE.md)\nfor details.\n\n---\n\n## References\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphilboileau%2Fcvcovest","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphilboileau%2Fcvcovest","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphilboileau%2Fcvcovest/lists"}