Nothing
sumInverseCorr()
sumInverseCorr()
based on direction
eclairs()
, if svd()
fails fall back on irlba()
sumInverseCorr()
has upper bound of p
dmult()
instead of transposingnu
to give correlation matrix close to having diagonals 1mahalanobisDistance()
averageCorr()
, averageCorrSq()
and sumInverseCorr()
alpha
parameter to quadForm()
n.samples
argument to \code{eclairs()}eclairs_sq
irlba
for SVD instead of PRIMME
series_start_total()
and use it in estimate_lambda_eb()
for partial SVDaverageCorr()
x.ri
, y.ri
fastcca()
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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