pblm-package

Fitting bivariate additive marginal regression models 
for categorical responses via penalized maximum likelihood estimation.

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.

Marco ENEA

pblm

Bivariate Additive Marginal Regression for Categorical Responses

Marco Enea

Mikis Stasinopoulos

Robert Rigby

pblm-package function

Marco Enea, with contributions by Mikis Stasinopoulos and Robert Rigby
Maintainer: Marco Enea &lt;marco.enea@unipa.it&gt;

Package:	pblm
Type:	Package
Version:	0.1-12
Date:	2025-06-18
License:	GPL (>= 2)

pblm-package: Bivariate Additive Marginal Regression for Categorical Responses

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