Authors@R: person("Daniel", "Müllner", email =
"fastcluster@math.stanford.edu", role = c("aut", "cph", "cre"))

"muellner@math.stanford.edu", role = c("aut", "cph", "cre"))
It provides the same functionality with the benefit of a much

"daniel@danifold.net", role = c("aut", "cph", "cre"))
both R and Python. It implements fast hierarchical, agglomerative
clustering routines. Part of the functionality is designed as drop-in
replacement for existing routines: “linkage” in the SciPy package
“scipy.cluster.hierarchy”, “hclust” in R's “stats” package, and the
“flashClust” package. It provides the same functionality with the
benefit of a much faster implementation. Moreover, there are
memory-saving routines for clustering of vector data, which go beyond
what the existing packages provide. For information on how to install
the Python files, see the file INSTALL in the source distribution.

Title: Fast Hierarchical Clustering Routines for R and Python

Authors@R: person("Daniel", "Müllner", email = "daniel@danifold.net", role = c("aut", "cph", "cre"))
replacement for existing routines: "linkage" in the SciPy package
"scipy.cluster.hierarchy", "hclust" in R's "stats" package, and the
"flashClust" package. It provides the same functionality with the

replacement for existing routines: linkage() in the SciPy package
'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
'flashClust' package. It provides the same functionality with the

Copyright: Until package version 1.1.23: © 2011 Daniel Müllner
<http://danifold.net>. All changes from version 1.1.24 on: ©

Title: Fast Hierarchical Clustering Routines for R and 'Python'
Authors@R: c(person("Daniel", "Müllner", email = "daniel@danifold.net", role = c("aut", "cph", "cre")),
both R and 'Python'. It implements fast hierarchical, agglomerative
replacement for existing routines: linkage() in the 'SciPy' package
the 'Python' files, see the file INSTALL in the source distribution.
Based on the present package, Christoph Dalitz also wrote a pure 'C++'

Title: Significant Interval Discovery with Categorical Covariates
Author: Felipe Llinares Lopez, Dean Bodenham
Description: A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.