A package accompanying the book Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data by Bergsma, Croon, & Hagenaars, 2009. It’s purpose is fitting and testing of marginal models.

We propose a new method to test conditional independence of two real random variables $Y$ and $Z$ conditionally on an arbitrary third random variable $X$. The partial copula is introduced, defined as the joint distribution of $U=F_{Y|X}(Y|X)$ and $V=F_{Z|X}(Z|X)$. We call this transformation of...

The standard two-variable chi-square test is typically consistent for all alternatives to independence, but effectively treats the data as nominal which may lead to loss of power for ordinal data. Alternatively, a test based on Kendall's tau does take ordinality into account, but only has power...

A possible drawback of the ordinary correlation coefficient p for two real random variables X and Y is that zero correlation does not imply independence. In this paper we introduce a new correlation coefficient p* which assumes values between zero and one, equalling zero iff the two variables...

Models defined by a set of conditional independence restrictions play an important role in statistical theory and applications, especially, but not only, in graphical modelling. In this paper we identify a subclass of these consisting of hierarchical marginal log-linear models, as defined by...

We discuss two parameterizations of models for marginal independencies for discrete distributions which are representable by bi-directed graph models, under the global Markov property. Such models are useful data analytic tools especially if used in combination with other graphical models. The...

When the sample size is small compared to the number of cells in a contingency table, maximum likelihood estimates of logit parameters and their associated standard errors may not exist or may be biased. This problem is usually solved by “smoothing†the estimates, assuming a certain...