Algorithms of distance-based k-medoids clustering:
simple and fast k-medoids, ranked k-medoids, and increasing number
of clusters in k-medoids. Calculate distances for mixed variable
data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and
Ahmad-Dey. Cluster validations apply internal and relative
criteria. The internal criteria include silhouette index and
shadow values. The relative criterium applies bootstrap procedure
producing a heatmap with a flexible reordering matrix algorithm
such as ward, complete, or centroid linkages. The cluster result
can be plotted in a marked barplot or pca biplot.

Algorithms of distance-based k-medoids clustering: simple and fast
k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids.
Calculate distances for mixed variable data such as Gower, Podani, Wishart,
Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and
relative criteria. The internal criteria includes silhouette index and shadow
values. The relative criterium applies bootstrap procedure producing a heatmap
with a flexible reordering matrix algorithm such as complete, ward, or average
linkages. The cluster result can be plotted in a marked barplot or pca biplot.

Fast K-Mer Counting and Clustering for Biological Sequence
Analysis
person("Shaun", "Wilkinson", role = c("aut", "cre"),
email = "shaunpwilkinson@gmail.com")
Contains tools for rapidly computing distance matrices
and clustering large sequence datasets using fast alignment-free
k-mer counting and recursive k-means partitioning.
See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005>
for a review of k-mer counting methods and applications for
biological sequence analysis.
openssl, phylogram, Rcpp (>= 0.12.13), stats

ape (>= 4.0), dendextend, knitr, rmarkdown, testthat

Kaplan-Meier multiple imputation for the analysis of cumulative
incidence functions in the competing risks setting
The package performs a Kaplan-Meier multiple imputation to
recover the missing potential censoring information from
competing risks events, so that standard right-censored methods
could be applied to the imputed data sets to perform analyses
of the cumulative incidence functions.