Generate balance tables and plots for covariates of groups preprocessed through 
             matching, weighting or subclassification, for example, using propensity scores. Includes 
             integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 
             'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing 
             functions. Users can also specify data for balance assessment not generated through 
             the above packages. Also included are methods for assessing balance in clustered or 
             multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Enrichment strategies play a critical role in modern clinical trial design, especially as precision medicine advances the focus on patient-specific efficacy. Recent developments in enrichment design have introduced biomarker randomness and accounted for the correlation structure between treatment effect and biomarker, resulting in a two-stage threshold enrichment design. We propose novel two-stage enrichment designs capable of handling two or more continuous biomarkers.
    See Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report.

Implementation of a probabilistic method for biclustering
    adapted to overdispersed count data. It is a Gamma-Poisson Latent
    Block Model.  It also implements two selection criteria in order to
    select the number of biclusters.

Provides functions for cobin and micobin regression models, a new family of generalized linear models for continuous proportional data (Y in the closed unit interval [0, 1]). It also includes an exact, efficient sampler for the Kolmogorov-Gamma random variable. For details, see Lee et al. (2025+) <doi:10.48550/arXiv.2504.15269>.

Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.

Contains the R functions needed to perform Cluster-Of-Clusters Analysis (COCA)  and Consensus Clustering (CC). For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.