Random Cluster Generation (with Specified Degree of Separation)
We developed the clusterGeneration package to provide functions
for generating random clusters, generating random
covariance/correlation matrices,
calculating a separation index (data and population version)
for pairs of clusters or cluster distributions, and 1-D and 2-D
projection plots to visualize clusters.  The package also
contains a function to generate random clusters based on
factorial designs with factors such as degree of separation,
number of clusters, number of variables, number of noisy
variables.

Identifying clusters in genomics data by recursive partitioning
The Partitioning Algorithm based on Recursive Thresholding
(PART) is used to recursively uncover clusters and subclusters
in the data. Functionality is also available for visualization
of the clustering.

Tools for Clustering High-Dimensional Data
c(
person(given="Jakob",family="Raymaekers", role = c("aut","cre"),
email="j.raymaekers@maastrichtuniversity.nl"),
person(given="Ruben",family="Zamar", role = c("aut"))
)
Jakob Raymaekers [aut, cre],
Ruben Zamar [aut]
Tools for clustering high-dimensional data.
In particular, it contains the methods described in
<doi:10.1093/bioinformatics/btaa243>,
<arXiv:2010.00950>.
Rcpp (>= 1.0.7), stats, mclust, Ckmeans.1d.dp, cluster

Power calculations for cluster-randomized crossover trials
Nicholas G. Reich <nick.reich@gmail.com>, Daniel Obeng
<dobeng@gmail.com>
This package enables researchers to calculate power for
cluster-randomized crossover trials by employing a
simulation-based approach.  A particular study design is
specified, with fixed sample sizes for all clusters and an
assumed treatment effect, and the empirical power for that
study design is calculated by simulating hypothetical datasets.

Power calculations for cluster-randomized and cluster-randomized
crossover trials

Nicholas G. Reich <nick@schoolph.umass.edu>, Daniel Obeng <dobeng@gmail.com>
This package enables researchers to calculate power for cluster-randomized crossover trials by employing a simulation-based approach.  A particular study design is specified, with fixed sample sizes for all clusters and an assumed treatment effect, and the empirical power for that study design is calculated by simulating hypothetical datasets.