Description: This is primarily meant as an implementation of generalized blockmodeling for valued networks.
In addition, measures of similarity or dissimilarity based on structural equivalence and
regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted:

Imports: stats, methods, Matrix, parallel
Suggests: sna, doRNG, doParallel, foreach

Authors@R: c(person(given = "Aleš", family = "Žiberna", email = "ales.ziberna@gmail.com", role = c("aut", "cre")), person( family ="Cugmas", given = "Marjan", email = "marjan.cugmas@fdv.uni-lj.si", role = c("ctb")))

Title: Latent and Stochastic Block Model Estimation by a 'V-EM'
Author: INRA, Jean-Benoist Leger <jbleger@bordeaux.inra.fr>
Description: Latent and Stochastic Block Model estimation by a V-EM algorithm
Various probability distribution are provided (Bernoulli,
Poisson...), with or without covariates.
Depends: Rcpp (>= 0.10.6), parallel, methods, digest

Description: Latent and Stochastic Block Model estimation by a Variational EM algorithm.

Author: Jean-Benoist Leger <jbleger@hds.utc.fr>, Pierre Barbillon <pierre.barbillon@agroparistech.fr>, Julien Chiquet <julien.chiquet@inrae.fr>
Imports: Rcpp, parallel, methods, digest

Title: Randomization for block random clinical trials
Description: Create randomizations for block random clinical trials.  Can also produce a pdf file of randomization cards.
Packaged: Sat Aug 12 21:10:55 2006; Greg

Packaged: Sat Feb  2 13:22:41 2008; Greg

Description: Create randomizations for block random clinical trials.
Can also produce a pdf file of randomization cards.

Title: Randomization for Block Random Clinical Trials

Title: Nested block designs for unstructured treatments
Description: Nested block designs for unstructured treatment sets where blocks
can be repeatedly nested and treatments can have different levels of
replication. Blocks strata are optimized hierarchically with each set of
nested blocks optimized within the levels of the preceding set. Block sizes
are equal if the number of blocks exactly divides the number of plots,
otherwise they differ by at most one plot. The design output is a data
table giving a randomised allocation of treatments to blocks together with
a plan table showing treatments in blocks and a set of blocks-by-treatments
incidence matrices, one for each blocks stratum.