sumInverseCorr()sumInverseCorr() based on
directioneclairs(), if svd() fails fall back on
irlba()sumInverseCorr() has upper bound of pdmult() instead of transposingnu to give correlation matrix close to having
diagonals 1mahalanobisDistance()averageCorr(), averageCorrSq() and
sumInverseCorr()alpha parameter to quadForm()n.samples argument to eclairs_sqirlba for SVD instead of PRIMMEseries_start_total() and use it in
estimate_lambda_eb() for partial SVDaverageCorr()x.ri, y.rifastcca() and cca() give equivalent
resultssqrt(1-lambda.x)*sqrt(1-lambda.y)fastcca() and cca()kappa() to compute condition numberlogDet() to compute log determinantcca() for canonical correlation analysisgetCov() and getCor() now have lambda
argumentplot() for eclairs shows arrow on right for zero
eigen-valuesestimate_lambda_eb() now returns logML for estimated or
specified lambdaeclairs()whiten() that combines eclairs() and
decorrelate() into one function calleclairs_corMat() to perform decomposition on
correlation matrixreform_decomp2() to work with result of
eclairs_corMat()estimate_lambda_eb() to perform empirical Bayes
estimation of lambdaplot() for eclairsreform_decomp()lm_eclairs() and
lm_each_eclairs()
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