Package: cvCovEst 1.2.2
cvCovEst: Cross-Validated Covariance Matrix Estimation
An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.
Authors:
cvCovEst_1.2.2.tar.gz
cvCovEst_1.2.2.zip(r-4.5)cvCovEst_1.2.2.zip(r-4.4)cvCovEst_1.2.2.zip(r-4.3)
cvCovEst_1.2.2.tgz(r-4.4-any)cvCovEst_1.2.2.tgz(r-4.3-any)
cvCovEst_1.2.2.tar.gz(r-4.5-noble)cvCovEst_1.2.2.tar.gz(r-4.4-noble)
cvCovEst_1.2.2.tgz(r-4.4-emscripten)cvCovEst_1.2.2.tgz(r-4.3-emscripten)
cvCovEst.pdf |cvCovEst.html✨
cvCovEst/json (API)
NEWS
# Install 'cvCovEst' in R: |
install.packages('cvCovEst', repos = c('https://philboileau.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/philboileau/cvcovest/issues
covariance-matrix-estimationcross-validationhigh-dimensional-statisticsnonparametric-statistics
Last updated 9 months agofrom:f6ef42b32e. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 13 2024 |
R-4.5-win | OK | Nov 13 2024 |
R-4.5-linux | OK | Nov 13 2024 |
R-4.4-win | OK | Nov 13 2024 |
R-4.4-mac | OK | Nov 13 2024 |
R-4.3-win | OK | Nov 13 2024 |
R-4.3-mac | OK | Nov 13 2024 |
Exports:adaptiveLassoEstbandingEstcvCovEstcvFrobeniusLosscvMatrixFrobeniusLosscvScaledMatrixFrobeniusLossdenseLinearShrinkEstis.cvCovEstlinearShrinkEstlinearShrinkLWEstnlShrinkLWEstpoetEstrobustPoetEstsampleCovEstscadEstspikedFrobeniusShrinkEstspikedOperatorShrinkEstspikedSteinShrinkEsttaperingEstthresholdingEst
Dependencies:abindassertthatbackportsbootbroomcarcarDataclicodetoolscolorspacecoopcorrplotcowplotcpp11data.tableDerivdigestdoBydplyrfansifarverFormulafuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegridExtragtableisobandlabelinglatticelifecyclelistenvlme4magrittrMASSMatrixMatrixModelsmatrixStatsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivorigamiparallellypbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackrlangRMTstatRSpectrarstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr