corpcor: Efficient Estimation of Covariance and (Partial) Correlation. This package implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schäfer and Strimmer (2005) and Opgen-Rhein and Strimmer (2007). The approach is both computationally as well as statistically very efficient, it is applicable to ”small n, large p” data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix.

References in zbMATH (referenced in 22 articles )

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  1. Braverman, Amy; Hobbs, Jonathan; Teixeira, Joaquim; Gunson, Michael: Post hoc uncertainty quantification for remote sensing observing systems (2021)
  2. Bod’a, Martin: Stochastic sensitivity analysis of concentration measures (2017)
  3. Ternès, Nils; Rotolo, Federico; Heinze, Georg; Michiels, Stefan: Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces (2017)
  4. Kruppa, Jochen; Kramer, Frank; Beißbarth, Tim; Jung, Klaus: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments (2016)
  5. Reiner-Benaim, Anat: Scan statistic tail probability assessment based on process covariance and window size (2016)
  6. Amatya, Anup; Demirtas, Hakan: Simultaneous generation of multivariate mixed data with Poisson and normal marginals (2015)
  7. Ullah, Insha; Jones, Beatrix: Regularised MANOVA for high-dimensional data (2015)
  8. Demirtas, Hakan; Amatya, Anup; Doganay, Beyza: Binnor: an (\mathcalR) package for concurrent generation of binary and normal data (2014)
  9. Delaigle, Aurore; Hall, Peter: Effect of heavy tails on ultra high dimensional variable ranking methods (2012)
  10. Tong, Tiejun; Jang, Homin; Wang, Yuedong: James-Stein type estimators of variances (2012)
  11. Boulesteix, Anne-Laure; Guillemot, Vincent; Sauerbrei, Willi: Use of pretransformation to cope with extreme values in important candidate features (2011)
  12. Ahdesmäki, Miika; Strimmer, Korbinian: Feature selection in omics prediction problems using cat scores and false nondiscovery rate control (2010)
  13. Hall, Peter; Miller, Hugh: Modeling the variability of rankings (2010)
  14. Ackermann, Marit; Strimmer, Korbinian: A general modular framework for gene set enrichment analysis (2009) ioport
  15. Cao, Jing; Xie, Xian-Jin; Zhang, Song; Whitehurst, Angelique; White, Michael A.: Bayesian optimal discovery procedure for simultaneous significance testing (2009) ioport
  16. Hall, Peter; Titterington, D. M.; Xue, Jing-Hao: Tilting methods for assessing the influence of components in a classifier (2009)
  17. Hausser, Jean; Strimmer, Korbinian: Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks (2009)
  18. Marot, Guillemette; Foulley, Jean-Louis; Jaffrézic, Florence: A structural mixed model to shrink covariance matrices for time-course differential gene expression studies (2009)
  19. Zhang, Song; Cao, Jing: A close examination of double filtering with fold change and t test in microarray analysis (2009) ioport
  20. Zhang, Xinyu; Chen, Ti; Wan, Alan T. K.; Zou, Guohua: Robustness of Stein-type estimators under a non-scalar error covariance structure (2009)

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