Fitting block ERGM given the block structure on social networks
Xiaolin Yang,Stephen Fienberg, Alessandro Rinaldo, Han Liu,Michael Rosenblum
This package is an extension to the "ergm" package which implements the block ergms.

c(
person("Xiaolin","Yang",role=c("aut","cre"), email="xyang@stat.cmu.edu"),
person("Stephen E.","Fienberg", role=c("aut"),email="fienberg@stat.cmu.edu"),
person("Alessandro","Rinaldo",role=c("aut"),email="rinaldo@stat.cmu.edu"),
person("Han","Liu",role=c("aut"),email="hanliu@princeton.edu"),
person("Michael","Rosenblum",role=c("aut"),email="mrosen@jhu.edu"))
Xiaolin Yang [aut, cre],
Stephen E. Fienberg [aut],
Alessandro Rinaldo [aut],
Han Liu [aut],
Michael Rosenblum [aut]

Binomial linear and linear-expit regression
R (>= 2.10.1), stats, methods, BB, MASS, numDeriv, alabama (>=
2010.9-1)
General additive regression models for binary cohort data
which use constrained maximum likelihood for estimation.

General additive regression models for binary cohort data which use constrained maximum likelihood for estimation.

Implements regression models for binary data on the
absolute risk scale. These models are applicable to cohort and
populaiton-based case-control data.

Implements regression models for binary data on the
absolute risk scale. These models are applicable to cohort and
population-based case-control data.

Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.

Maximum a posteriori estimation for linear and generalized
linear mixed-effects models in a Bayesian setting. Extends lme4
by Douglas Bates and Martin Maechler.

Maximum a posteriori estimation for linear and generalized
linear mixed-effects models in a Bayesian setting. Extends
lme4 by Douglas Bates and Martin Maechler.

Maximum a posteriori estimation for linear and generalized
linear mixed-effects models in a Bayesian setting. Extends
lme4 by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker.