Implementation of a Bayesian two-way latent structure model for integrative genomic clustering.  The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation.  A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler.  The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density.  Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources.  Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU.  See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, <doi:10.1101/387076>, Cold Spring Harbor Laboratory" at <https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf> for model details.

The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming 
    taxons in Dutch Watermanagement. This package makes it easier to use the 
    TWN-list for ecological analyses. It  consists of two parts. First it makes the 
    TWN-list itself available in R. Second, it has a few functions that make it 
    easy to perform some basic and often recurring tasks for checking and consulting
    taxonomic data from the TWN-list.

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.