Block, assign, and diagnose potential interference in randomized experiments
Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units.  Maximum, minimum, or an allowable range of differences between units on one variable can be set.  Randomly assign units to treatment conditions.  Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files.

Block, assign, and diagnose potential interference in randomized
experiments
Blocks units into experimental blocks, with one unit per
treatment condition, by creating a measure of multivariate
distance between all possible pairs of units.  Maximum,
minimum, or an allowable range of differences between units on
one variable can be set.  Randomly assign units to treatment
conditions.  Diagnose potential interference between units
assigned to different treatment conditions.  Write outputs to
.tex and .csv files.

Block, Assign, and Diagnose Potential Interference in Randomized
Experiments

Co-Clustering package for Binary, contingency and continuous
data-sets.
Parmeet Bhatia and Serge Iovleff and G<c3><a9>rard Goavert,
with contributions from Christophe Biernacki and Gilles Celeux.
This package allows to co-cluster Binary, contingency and
continuous data-sets. It also provides utility functions to
visualize the results. This package may be useful for various
applications in fields of Data mining, Biology etc.
'Strategy.R' 'optionclasses.R' 'summary.R' 'plot.R' 'onload.R'
'Rcoclust.R' 'cocluster.R'

Co-Clustering package for Binary, Contingency and Continuous
data-sets.
Parmeet Bhatia & Serge Iovleff & Gerard Goavert, with
contributions from Christophe Biernacki and Gilles Celeux.
Simultaneous clustering of rows and columns, usually
designated by biclustering, co-clustering or block clustering,
is an important technique in two way data analysis. It consists
of estimating a mixture model which takes into account the
block clustering problem on both the individual and variables
sets. The blockcluster package provides a bridge between the
C++ core library and the R statistical computing environment.
This package allows to co-cluster Binary, contingency and
continuous data-sets. It also provides utility functions to
visualize the results. This package may be useful for various
applications in fields of Data mining,Information retrieval,
Biology, computer vision and many more.