Implementations of (1) mutual dependence measures and mutual independence tests in
Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
(2) independent component analysis methods based on mutual dependence measures in
Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>
and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
(3) conditional mean dependence measures and conditional mean independence tests in
Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>
and Park, T., et al. (2015) <doi:10.1214/15-EJS1047>.

Implementations of (1) mutual dependence measures and mutual independence tests in
Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
(2) independent component analysis methods based on mutual dependence measures in
Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>
and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
(3) conditional mean dependence measures and conditional mean independence tests in
Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>,
Park, T., et al. (2015) <doi:10.1214/15-EJS1047>,
and Lee, C. E., and Shao, X. (2017) <doi:10.1080/01621459.2016.1240083>.
'EDMeasure-package.R' 'cmdm_functions.R' 'pmdd.R' 'mdd.R'
'cmdm_test.R' 'mdc.R' 'mddm.R' 'mdm.R' 'mdm_ica_functions.R'
'mdm_ica.R' 'mdm_test.R' 'pmdc.R'

Implements the EDNE-Test for Equivalence
Package implements the EDNE-test for equivalence
according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
"EDNE" abbreviates "Euclidean Distance between the
Non-standardized Expected values".
The EDNE-test for equivalence is a multivariate two-sample equivalence test.
Distance measure of the test is the Euclidean distance.
The test is an asymptotically valid test for the family of distributions
fulfilling the assumptions of the multivariate central limit theorem
(see Hoffelder et al.,2015).
The function EDNE.EQ() implements the EDNE-test for equivalence
according to Hoffelder et al. (2015).
The function EDNE.EQ.dissolution.profiles() implements a variant
of the EDNE-test for equivalence analyses of dissolution profiles
(see Suarez-Sharp et al.,2020 <DOI:10.1208/s12248-020-00458-9>).
EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the
differences of the expected values of both dissolution profile populations
is statistically significantly smaller than 10 [\% of label claim].
The current regulatory standard approach for equivalence analyses of
dissolution profiles is the similarity factor f2.
The statistical hypotheses underlying EDNE.EQ.dissolution.profiles()
coincide with the hypotheses for f2 (see Hoffelder et al.,2015,
Suarez-Sharp et al., 2020).