Nonparametrics with Clustered Binary and Multinomial Data
Create a `CBdata' object from a data frame.
Create a `CMdata' object from a data frame.
Internal CorrBin objects
Nonparametrics for Correlated Binary and Multinomial Data
Extract from a CBData or CMData object
GEE-based trend test
Estimate joint event probabilities for multinomial data
Distribution of the number of responses assuming marginal compatibilit...
Test the assumption of marginal compatibility
Extract correlation coefficients from joint probability arrays
Functions for generating multinomial outcomes
Finding the NOSTASOT dose
Parametric distributions for correlated binary data
Generate random correlated binary data
Generate a random CMData object
Read data from external file into a CBData object
Read data from external file into a CMData object
Rao-Scott trend test
Likelihood-ratio test statistic
Order-restricted MLE assuming marginal compatibility
Likelihood ratio test of stochastic ordering
Control values for order-constrained fit
Test for increasing trend with correlated binary data
Extract univariate marginal probabilities from joint probability array...
Unwrap a clustered object
Implements non-parametric analyses for clustered binary and multinomial data. The elements of the cluster are assumed exchangeable, and identical joint distribution (also known as marginal compatibility, or reproducibility) is assumed for clusters of different sizes. A trend test based on stochastic ordering is implemented. Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>; George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.