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. (2026) <doi:10.1080/01621459.2026.2626081>.

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

A 'shiny' app to discover cocktails. The
    app allows one to search for cocktails by ingredient,
    filter on rating, and number of ingredients. The
    package also contains data with the ingredients of
    nearly 26 thousand cocktails scraped from the web.

A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2019). "Clustering dependent observations with copula functions". Statistical Papers, 60, p.35-51. <doi:10.1007/s00362-016-0822-3>.

Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modelled via Poisson and Generalized Poisson innovations. Regression effects can be incorporated through time varying innovation rates. The models are described in Jung and Tremayne (2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model assessment tools are presented in Czado et al. (2009) <doi:10.1111/j.1541-0420.2009.01191.x> and, Tsay (1992) <doi:10.2307/2347612>.