Man pages for decorrelate
Decorrelation Projection Scalable to High Dimensional Data
| autocorr.mat | Create auto-correlation matrix |
| cca | Canonical correlation analysis |
| cov_transform | Estimate covariance matrix after applying transformation |
| decorrelate | Decorrelation projection |
| dmult | Multiply by diagonal matrix |
| eclairs | Estimate covariance/correlation with low rank and shrinkage |
| eclairs-class | Class eclairs |
| eclairs_corMat | Estimate covariance/correlation with low rank and shrinkage |
| eclairs_sq | Compute eclairs decomp of squared correlation matrix |
| fastcca | Fast canonical correlation analysis |
| fastcca-class | Class fastcca |
| getCov | Get full covariance/correlation matrix from eclairs |
| getShrinkageParams | Estimate shrinkage parameter by empirical Bayes |
| getWhiteningMatrix | Get whitening matrix |
| kappa | Compute condition number |
| lm_each_eclairs | Fit linear model on each feature after decorrelating |
| lm_eclairs | Fit linear model after decorrelating |
| logDet | Evaluate the log determinant |
| mahalanobisDistance | Mahalanobis Distance |
| mult_eclairs | Multiply by eclairs matrix |
| optimal_SVHT_coef | Optimal Hard Threshold for Singular Values |
| plot-eclairs-method | Plot eclairs object |
| quadForm | Evaluate quadratic form |
| reform_decomp | Recompute eclairs after dropping features |
| rmvnorm_eclairs | Draw from multivariate normal and t distributions |
| summarizeCorr | Summarize correlation matrix |
| sv_threshold | Singular value thresholding |
| whiten | Decorrelation projection + eclairs |
