Clinical Data Interchange Standards Consortium (CDISC) Standard Data
    Tabulation Model (SDTM) controlled terminology, 2025-03-25.
    Source: <https://evs.nci.nih.gov/ftp1/CDISC/SDTM/>.

A series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis.

Provides a set of tools to assist statistical programmers 
    in validating Study Data Tabulation Model (SDTM) domain data sets. 
    Statistical programmers are required to validate that a SDTM data set domain 
    has been programmed correctly, per the SDTM Implementation Guide (SDTMIG) by 
    'CDISC' (<https://www.cdisc.org/standards/foundational/sdtmig>), 
    study specification, and study protocol using a process called double 
    programming. Double programming involves two different programmers 
    independently converting the raw electronic data cut (EDC) data into a SDTM 
    domain data table and comparing their results to ensure accurate 
    standardization of the data. One of these attempts is termed 'production' 
    and the other 'validation'. Generally, production runs are the official 
    programs for submittals and these are written in 'SAS'. Validation runs can 
    be programmed in another language, in this case 'R'.

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.