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.

A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data.