# SE-Test for Equivalence

Implements the SE-test for equivalence
according to Hoffelder et al. (2015) .
The SE-test for equivalence is a multivariate two-sample equivalence test.
Distance measure of the test is the sum of standardized differences
between the expected values or in other words: the sum of effect sizes (SE)
of all components of the two multivariate samples.
The test is an asymptotically valid test for normally distributed data
(see Hoffelder et al.,2015).
The function SE.EQ() implements the SE-test for equivalence
according to Hoffelder et al. (2015).
The function SE.EQ.dissolution.profiles() implements a variant
of the SE-test for equivalence for similarity analyses of dissolution
profiles as mentioned in Suarez-Sharp et al.(2020)
). The equivalence margin used in
SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ
approach according to Hoffelder (2019) )
by means of a systematic shift in location
of 10 [\% of label claim] of both dissolution profile populations.
SE.EQ.dissolution.profiles() checks whether the weighted mean of the
differences of the expected values of both dissolution profile populations
is statistically significantly smaller than 10 [\% of label claim]. The
weights are built up by the inverse variances.