A Fast Solver for Parameterized LP Problems, Constrained L1
Minimization Approach to Sparse Precision Matrix Estimation and
Dantzig Selector
Haotian Pang, Di Qi, Han Liu and Robert Vanderbei
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
solver using parametric simplex method.

Fast hierarchical clustering routines for R and Python
c(person("Daniel", "Müllner", email =
"fastcluster@math.stanford.edu"))
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.

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 “flashClust“ 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.