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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 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()
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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