Computes confidence intervals for single proportions, for
differences in proportions, for an odds-ratio and for the
relative risk in a 2x2 table
Computes confidence intervals for single proportions and
confidence intervals for differences in proportions in a 2x2
table

Computes confidence intervals for proportions, differences in
proportions, an odds-ratio and the relative risk in a 2x2 table
Computes confidence intervals for single proportions, for
differences in proportions, for an odds-ratio and for the
relative risk in a 2x2 table. Functions available for dependent
and independent data.

Various confidence interval methods for proportions
Computes confidence intervals for single proportions and
differences of dependent and independent proportions

Computes two-sample confidence intervals for single, paired and independent proportions

Various Confidence Interval Methods for Proportions
Computes two-sample confidence intervals for single, paired and independent proportions.

John Michael O Ranola, Kenneth Lange, Steve Horvath, Peter Langfelder
This package implements propensity clustering and
decomposition. Propensity decomposition can be viewed on the
one hand as a generalization of the eigenvector-based
approximation of correlation networks, and on the other hand as
a generalization of random multigraph models and
conformity-based decompositions.

R (>= 3.0.0), flashClust, dynamicTreeCut
Implementation of propensity clustering and
decomposition. Propensity decomposition can be viewed on the
one hand as a generalization of the eigenvector-based
approximation of correlation networks, and on the other hand as
a generalization of random multigraph models and
conformity-based decompositions.

R (>= 3.0.0), fastcluster, dynamicTreeCut
Implementation of propensity clustering and
decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>.
Propensity decomposition can be viewed on the
one hand as a generalization of the eigenvector-based
approximation of correlation networks, and on the other hand as
a generalization of random multigraph models and
conformity-based decompositions.