Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.

The subplex algorithm for unconstrained optimization, developed by Tom Rowan.

Estimation of copula using ranks and subsampling. The main feature of this method is that simulation studies show a low sensitivity to dimension, on realistic cases.

Functions for computing test subscores using different
    methods in both classical test theory (CTT) and item response theory (IRT). This
    package enables three types of subscoring methods within the framework of CTT
    and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) 
    <doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008) 
    <doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI; Yen, 1987) 
    <https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>. 
    It also includes functions to compute Proportional Reduction 
    of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to 
    examine whether test subscores are of added value. In addition, the package includes 
    a function to assess the local independence assumption of IRT with
    Yen's Q3 statistic (Yen, 1984 <doi:10.1177/014662168400800201>; Yen, 1993 
    <doi:10.1111/j.1745-3984.1993.tb00423.x>).

Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation.
 The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup.
 The diagram may show thousands of them. The position of the dot in the diagram is determined by the
 sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The
 sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore,
 the diagram shows the line of no effect and the overall study results. For small subgroups, which
 are found on the left side of the plot, larger random deviations from the mean study effect are expected,
 while for larger subgroups only small deviations from the study mean can be expected to be chance findings.
 So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a
 kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.
 This approach was presented in Muysers (2020) <doi:10.1007/s43441-019-00082-6> and referenced in Ballarini (2020) <doi:10.1002/pst.2012>.
 New to version 3 is the Automatic Screening of one- or MUlti-factorial Subgroups (ASMUS) for documentation of the structured review of subgroup findings.