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CorrBin (version 1.6.2)

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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Install

install.packages('CorrBin')

Monthly Downloads

243

Version

1.6.2

License

GPL (>= 2)

Maintainer

Aniko Szabo

Last Published

August 30th, 2024

Functions in CorrBin (1.6.2)

Parametric distributions for correlated binary data

Functions for generating multinomial outcomes

Extract univariate marginal probabilities from joint probability arrays

Control values for order-constrained fit

Generate a random CMData object

Test for increasing trend with correlated binary data

Read data from external file into a CBData object

Unwrap a clustered object

Shell Toxicology data

Read data from external file into a CMData object

GEE-based trend test

Likelihood-ratio test statistic

Finding the NOSTASOT dose

Distribution of the number of responses assuming marginal compatibility.

Internal CorrBin objects

Developmental toxicology study of DEHP in mice

Extract from a CBData or CMData object

Order-restricted MLE assuming marginal compatibility

Create a `CMdata' object from a data frame.

Extract correlation coefficients from joint probability arrays

mc.test.chisq.CMData

Test the assumption of marginal compatibility

Generate random correlated binary data

CorrBin-package

Nonparametrics for Correlated Binary and Multinomial Data

Likelihood ratio test of stochastic ordering

Create a `CBdata' object from a data frame.

Estimate joint event probabilities for multinomial data

Rao-Scott trend test