Provides a lightweight container and exchange format for gene set collections. It stores gene set membership, names, gene identifiers, species, versions, and source metadata, with utilities for reading, writing, validating, and converting gene set data for enrichment analysis and related workflows.

Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".

Generalization of supervised principal component regression (SPCR; 
    Bair et al., 2006, <doi:10.1198/016214505000000628>) to support continuous, 
    binary, and discrete variables as outcomes and predictors 
    (inspired by the 'superpc' R package <https://cran.r-project.org/package=superpc>).

A group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.

A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. 
    See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.

A comprehensive package for structural multivariate
        function estimation using smoothing splines.