Title: A Fast Solver for Parameterized LP Problems, Constrained L1
Minimization Approach to Sparse Precision Matrix Estimation and
Author: Haotian Pang, Di Qi, Han Liu and Robert Vanderbei
Description: Provides a method of recovering the precision matrix efficiently
and solving for the dantzig selector by applying the parametric
simplex method.  The computation is based on a linear optimization
solver. It also contains a generic LP solver and a parameterized LP

Title: Fast hierarchical clustering routines for R and Python
Author@R: c(person("Daniel", "Müllner", email =
Description: This is a two-in-one package which provides interfaces to
both R and Python. It implements fast hierarchical clustering
routines on a set of dissimilarities. The R interface is
designed as a drop-in replacement for the function “hclust” in
the “stats” package and for the “fastClust“ package. It
provides the same functionality, with the benefit of a better
and much faster implementation. The Python interface is written
as a replacement for the “linkage” function in the
“scipy.cluster.hierarchy” module. Again, it provides the same
functionality as the original function but a much faster
implementation. For information on how to install the Python
files, see the file INSTALL in the source distribution.

the “stats” package and for the “flashClust“ package. It

provides the same functionality, with the benefit of a much
faster implementation. The Python interface is written as a
replacement for the “linkage” function in the

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