R package 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 Sch”afer 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.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
- Alfonso Iodice D’Enza, Angelos Markos, Davide Buttarazzi: The idm Package: Incremental Decomposition Methods in R (2018) not zbMATH
- 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)
- Reiner-Benaim, Anat: Scan statistic tail probability assessment based on process covariance and window size (2016)
- Amatya, Anup; Demirtas, Hakan: MultiOrd: an R package for generating correlated ordinal data (2015)
- Amatya, Anup; Demirtas, Hakan: Simultaneous generation of multivariate mixed data with Poisson and normal marginals (2015)
- Anup Amatya, Hakan Demirtas: OrdNor: An R Package for Concurrent Generation of Correlated Ordinal and Normal Data (2015) not zbMATH
- Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
- Ullah, Insha; Jones, Beatrix: Regularised MANOVA for high-dimensional data (2015)
- Marco Geraci: Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression (2014) not zbMATH
- Stefan Van Aelst; Gert Willems: Fast and Robust Bootstrap for Multivariate Inference: The R Package FRB (2013) not zbMATH