Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).

Implements the SE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    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) 
    <DOI:10.1208/s12248-020-00458-9>). The equivalence margin used in 
    SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ
    approach according to Hoffelder (2019) <DOI:10.1002/bimj.201700257>) 
    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.

A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, <doi:10.1017/S0016672303006141>).