Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

Classical methods for combining summary data from genome-wide
    association studies (GWAS) only use marginal genetic effects and power can
    be compromised in the presence of heterogeneity. 'subgxe' is a R package
    that implements p-value assisted subset testing for association (pASTA),
    a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA
    generalizes association analysis based on subsets by incorporating
    gene-environment interactions into the testing procedure.

Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates.  The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.

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 <http://www.netlib.org/opt/subplex.tgz>.

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>).