Comprehensive functions to calculate sample size and power for 
    clinical trials with two co-primary endpoints. The package supports five 
    endpoint combinations: two continuous endpoints (Sozu et al. 2011 
    <doi:10.1080/10543406.2011.551329>), two binary endpoints using asymptotic 
    methods (Sozu et al. 2010 <doi:10.1002/sim.3972>) and exact methods (Homma 
    and Yoshida 2025 <doi:10.1177/09622802251368697>), mixed continuous and 
    binary endpoints (Sozu et al. 2012 <doi:10.1002/bimj.201100221>), and mixed 
    count and continuous endpoints (Homma and Yoshida 2024 
    <doi:10.1002/pst.2337>). All methods appropriately account for correlation 
    between endpoints and provide both sample size and power calculation 
    capabilities.

Provides a calculator for the two-dimensional clinical Disease Activity 
    index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure 
    that complements the conventional clinical DAPSA score. The TwoDcDAPSA captures 
    residual variation in patient-reported outcomes (pain and patient global assessment) 
    and joint counts (swollen and tender) after adjusting for standardized cDAPSA using 
    natural spline coefficients derived from published models. Residuals are 
    standardized and combined with fixed principal component loadings to yield a 
    continuous PROs-Joint Contrast (PJC) score and quartile groupings. The package 
    applies pre-specified coefficients and loadings to new datasets but does not 
    estimate spline models or principal components itself.

Implements two-mode clustering (biclustering) using 
    genetic algorithms. The method was first introduced in 
    Hageman et al. (2008) <doi:10.1007/s11306-008-0105-7>. The 
    package provides tools for fitting, visualization, and 
    validation of two-mode cluster structures in data matrices.

Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and
  confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.