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| tawny-package | Clean Covariance Matrices Using Random Matrix Theory and Shrinkage Estimators for Portfolio Optimization |
| cor.clean | Remove noise from a correlation matrix using RMT to identify the noise |
| cor.empirical | Remove noise from a correlation matrix using RMT to identify the noise |
| cor.mean | Shrink the covariance matrix towards some global mean |
| cov.prior.cc | Shrink the covariance matrix towards some global mean |
| cov.prior.identity | Shrink the covariance matrix towards some global mean |
| cov.sample | Shrink the covariance matrix towards some global mean |
| cov.shrink | Shrink the covariance matrix towards some global mean |
| cov_sample | Shrink the covariance matrix towards some global mean |
| cov_shrink | Shrink the covariance matrix towards some global mean |
| deform | Remove noise from a correlation matrix using RMT to identify the noise |
| denoise | Remove noise from a correlation matrix using RMT to identify the noise |
| Denoiser | Remove noise from a correlation matrix using RMT to identify the noise |
| divergence | Measure the divergence and stability between two correlation matrices |
| divergence.kl | Measure the divergence and stability between two correlation matrices |
| divergence.stability | Measure the divergence and stability between two correlation matrices |
| divergence_lim | Measure the divergence and stability between two correlation matrices |
| EmpiricalDenoiser | Remove noise from a correlation matrix using RMT to identify the noise |
| ensure | Utility functions for creating portfolios of returns and other functions |
| getIndexComposition | Utility functions for creating portfolios of returns and other functions |
| getPortfolioReturns | Utility functions for creating portfolios of returns and other functions |
| KullbackLeibler | Measure the divergence and stability between two correlation matrices |
| normalize | Remove noise from a correlation matrix using RMT to identify the noise |
| optimizePortfolio | Optimize a portfolio using the specified correlation filter |
| p.optimize | Optimize a portfolio using the specified correlation filter |
| plotDivergenceLimit.kl | Measure the divergence and stability between two correlation matrices |
| RandomMatrixDenoiser | Remove noise from a correlation matrix using RMT to identify the noise |
| SampleDenoiser | Remove noise from a correlation matrix using RMT to identify the noise |
| shrinkage.c | Shrink the covariance matrix towards some global mean |
| shrinkage.intensity | Shrink the covariance matrix towards some global mean |
| shrinkage.p | Shrink the covariance matrix towards some global mean |
| shrinkage.r | Shrink the covariance matrix towards some global mean |
| ShrinkageDenoiser | Remove noise from a correlation matrix using RMT to identify the noise |
| sp500 | A (mostly complete) subset of the SP500 with 250 data points |
| sp500.subset | A subset of the SP500 with 200 data points |
| stability_lim | Measure the divergence and stability between two correlation matrices |
| tawny | Clean Covariance Matrices Using Random Matrix Theory and Shrinkage Estimators for Portfolio Optimization |