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>.

Density, distribution function, quantile function, 
    and random generation function, maximum likelihood estimation (MLE), 
    penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM),
    and median rank estimation (MEDRANK) for the two-parameter exponential distribution.
    MLE and PMLE are based on Mengjie Zheng (2013)<https://scse.d.umn.edu/sites/scse.d.umn.edu/files/mengjie-thesis_masters-1.pdf>.
    QM is based on Entisar Elgmati and Nadia Gregni (2016)<doi:10.5539/ijsp.v5n5p12>.
    MEDRANK is based on Matthew Reid (2022)<doi:10.5281/ZENODO.3938000>.
    In addition, the functions for computing the Kolmogorov-Smirnov (D) statistic, 
    the Cramér-von Mises statistic, and the Anderson–Darling statistic are provided.

Tools for constructing and analyzing two-phase experimental designs under correlated error structures. Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) <doi:10.2307/3001770>).

Provides functionality for designing and analysing two-phase 
    genetic association studies. Phase 1 data usually come from
    genome-wide association study (GWAS) results and we assume phase 2  data 
    will be part of a targeted genome sequencing or fine-mapping study. 
    At design stage, the package assists in selecting a subset of individuals 
    that will be sequenced for phase 2 via alternative approaches, including a 
    flexible genetic algorithm (GA) for near-optimal designs. Once phase 2 data 
    have been collected, the package implements methods to analyse phase 1 and 
    phase 2 data together using semi-parametric regression models via the 
    expectation-maximization (EM) algorithm. For more details see 
    Espin-Garcia, Craiu and Bull (2018) <doi:10.1002/gepi.22099> and 
    Espin-Garcia, Craiu and Bull (2021) <doi:10.1002/sim.9211>.