R: Decorrelation Projection Scalable to High Dimensional Data

# Decorrelation Projection Scalable to High Dimensional Data

## Documentation for package ‘decorrelate’ version 0.1.6.4

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

autocorr.mat | Create auto-correlation matrix
averageCorr | Summarize correlation matrix
averageCorrSq | Summarize correlation matrix
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
effVariance | Summarize correlation matrix
fastcca-class | Class fastcca
getCor | Get full covariance/correlation matrix from eclairs
getCor-method | Get full covariance/correlation matrix from eclairs
getCov | Get full covariance/correlation matrix from eclairs
getCov-method | Get full covariance/correlation matrix from eclairs
getShrinkageParams | Estimate shrinkage parameter by empirical Bayes
getWhiteningMatrix | Get whitening matrix
kappa-method | 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-method | Plot eclairs object
quadForm | Evaluate quadratic form
reform_decomp | Recompute eclairs after dropping features
rmvnorm_eclairs | Draw from multivariate normal and t distributions
sumInverseCorr | Summarize correlation matrix
sv_threshold | Singular value thresholding
tr | Summarize correlation matrix
whiten | Decorrelation projection + eclairs