Package index • decorrelate

All functions

autocorr.mat(): Create auto-correlation matrix

cov_transform(): Estimate covariance matrix after applying transformation

decorrelate(): Decorrelation projection

dmult(): Multiply by diagonal matrix

eclairs-class: Class eclairs

eclairs(): Estimate covariance/correlation with low rank and shrinkage

eclairs_corMat(): Estimate covariance/correlation with low rank and shrinkage

eclairs_sq(): Compute eclairs decomp of squared correlation matrix

fastcca-class: Class fastcca

getCov() getCor(): Get full covariance/correlation matrix from eclairs

getShrinkageParams(): Estimate shrinkage parameter by empirical Bayes

getWhiteningMatrix(): Get whitening matrix

kappa(<eclairs>): 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>): Plot eclairs object

quadForm(): Evaluate quadratic form

reform_decomp(): Recompute eclairs after dropping features

rmvnorm_eclairs(): Draw from multivariate normal and t distributions

averageCorr() averageCorrSq() sumInverseCorr() effVariance() tr(): Summarize correlation matrix

sv_threshold(): Singular value thresholding

whiten(): Decorrelation projection + eclairs