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")))
Aleš Žiberna [aut, cre],
Marjan Cugmas [ctb]

Latent and Stochastic Block Model Estimation by a 'V-EM'
Algorithm
Latent and Stochastic Block Model estimation by a V-EM algorithm
Various probability distribution are provided (Bernoulli,
Poisson...), with or without covariates.
Rcpp (>= 0.10.6), parallel, methods, digest

Latent and Stochastic Block Model estimation by a Variational EM algorithm.
Various probability distribution are provided (Bernoulli,
Poisson...), with or without covariates.

Jean-Benoist Leger <jbleger@hds.utc.fr>, Pierre Barbillon <pierre.barbillon@agroparistech.fr>, Julien Chiquet <julien.chiquet@inrae.fr>

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

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

Randomization for Block Random Clinical Trials

Nested block designs for unstructured treatments
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.

Nested Block Designs for Unstructured Treatments

Nested block designs for unstructured treatment sets where blocks
can be repeatedly nested and treatments can have different levels of
replication. Blocks are optimized hierarchically from the top down with each set of
nested blocks optimized within the levels of the preceding set of blocks. 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.