Bivariate Additive Marginal Regression for Categorical Responses
Description
Bivariate additive categorical regression via penalized maximum likelihood.
Under a multinomial framework, the method fits bivariate models where both
responses are nominal, ordinal, or a mix of the two. Partial proportional
odds models are supported, with flexible (non-)uniform association structures.
Various logit types and parametrizations can be specified for both marginals
and the association, including Dale’s model. The association structure can
be regularized using polynomial-type penalty terms. Additive effects are
modeled using P-splines. Standard methods such as summary(), residuals(),
and predict() are available.