Kaplan-Meier Simultaneous Confidence Band for the Survivor
Function
Computes and plots an exact nonparametric band for any user-specified level of confidence from a single-sample survfit object

'Yinyang' K-Means and K-NN using NVIDIA CUDA
K-means implementation is based on "Yinyang K-Means: A Drop-In Replacement of
the Classic K-Means with Consistent Speedup". While it introduces some overhead and many
conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd
algorithm. K-nearest neighbors employ the same triangle inequality idea and require
precalculated centroids and cluster assignments, similar to the flattened ball tree.
CUDA 8.0 tookit, OpenMP 4.0 capable compiler

'Yingyang' K-Means and K-NN using NVIDIA CUDA
K-means implementation is base on "Yingyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup". While it introduces some overhead and many conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd algorithm.  K-nearest neighbors employ the same triangle inequality idea and require precalculated centroids and cluster assignments, similar to the flattened ball tree.

person("Rafal", "Urniaz", comment = c(ORCID = "0000-0003-0192-2165"), email = "rafal.urniaz@gmail.com", role = c("aut", "cre"))
Contains tools to calculate similarity score matrix for DNA k-mers. The pairwise
similarity score is calculated using PAM or BLOSUM substitution matrix. The
results are evaluated by similarity score calculated by Needleman-Wunsch
(1970) <doi:10.1016/0022-2836(70)90057-4> global or Smith-Waterman
(1981) <doi:10.1016/0022-2836(81)90087-5> local alignment. Higher similarity
score indicates more similar sequences for BLOSUM and less similar sequences
for PAM matrix; 30, 40, 70, 120, 250 and 62, 45, 50, 62, 80, 100 matrix
versions are available for PAM and BLOSUM, respectively.

Distributed k-Means for Big Data using 'ddR' API
Distributed k-means clustering algorithm written using 'ddR' (Distributed Data Structures) API in the 'ddR' package.