Fast, Robust and Tunning-Free Regularized Covariance Matrix Estimators
How To Install?
library('devtools') install_github(repo = "davidnexer/impcov")
- Ledoit-Wolf (LW) Shrinkage Estimator 
- Oracle Approximating Shrinkage (OAS) Estimator 
- Schafer-Strimmer (SS) Shrinkage Estimator 
- Reduced-Rank Estimator based on Probabilistic PCA (PPCA) 
- Sparse Reduced-Rank Estimator based on Sparse Principal Components (SPC) 
- Sparse Estimator based on Penalized Likelihood with Quadratic Approximation (QUIC) 
- Regularization of a covariance matrix estimate given by the user;
- Weighted estimation based on sample weights given by the user.
 Ledoit, Olivier, and Michael Wolf. "A well-conditioned estimator for large-dimensional covariance matrices." Journal of multivariate analysis 88.2 (2004): 365-411.
 Chen, Yilun, et al. "Shrinkage algorithms for MMSE covariance estimation." Signal Processing, IEEE Transactions on 58.10 (2010): 5016-5029.
 Schäfer, Juliane, and Korbinian Strimmer. "A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics." Statistical applications in genetics and molecular biology 4.1 (2005).
 Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal component analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61.3 (1999): 611-622.
 Witten, Daniela M., Robert Tibshirani, and Trevor Hastie. "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis." Biostatistics (2009): kxp008.
 Hsieh, Cho-Jui, et al. "Sparse inverse covariance matrix estimation using quadratic approximation." Advances in Neural Information Processing Systems. 2011.