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CorrBin (version 1.6.1)

Nonparametrics with Clustered Binary and Multinomial Data

Description

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) ; George EO, Cheon K, Yuan Y, Szabo A (2016) .

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Version

Install

install.packages('CorrBin')

Monthly Downloads

207

Version

1.6.1

License

GPL (>= 2)

Maintainer

Aniko Szabo

Last Published

September 29th, 2023

Functions in CorrBin (1.6.1)

Extract from a CBData or CMData object

GEE-based trend test

CorrBin-package

Nonparametrics for Correlated Binary and Multinomial Data

Estimate joint event probabilities for multinomial data

Developmental toxicology study of DEHP in mice

Create a `CMdata' object from a data frame.

Create a `CBdata' object from a data frame.

Likelihood ratio test of stochastic ordering

Extract univariate marginal probabilities from joint probability arrays

Functions for generating multinomial outcomes

Extract correlation coefficients from joint probability arrays

Read data from external file into a CMData object

Control values for order-constrained fit

Generate random correlated binary data

Parametric distributions for correlated binary data

mc.test.chisq.CMData

Test the assumption of marginal compatibility

Generate a random CMData object

Shell Toxicology data

Finding the NOSTASOT dose

Test for increasing trend with correlated binary data

Distribution of the number of responses assuming marginal compatibility.

Rao-Scott trend test

Unwrap a clustered object

Read data from external file into a CBData object

Likelihood-ratio test statistic

Internal CorrBin objects

Order-restricted MLE assuming marginal compatibility