pblm: Bivariate Additive Marginal Regression for Categorical Responses

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.

Version:	0.1-12
Depends:	R (≥ 4.4.0), Matrix, lattice, splines, MASS
Imports:	methods
Published:	2025-06-19
DOI:	10.32614/CRAN.package.pblm
Author:	Marco Enea [aut, cre, cph], Mikis Stasinopoulos [ctb], Robert Rigby [ctb]
Maintainer:	Marco Enea <marco.enea at unipa.it>
BugReports:	https://github.com/MarcoEnea/pblm/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/MarcoEnea/pblm
NeedsCompilation:	no
Citation:	pblm citation info
CRAN checks:	pblm results

Documentation:

Reference manual:

pblm.pdf

Downloads:

Package source:	pblm_0.1-12.tar.gz
Windows binaries:	r-devel: not available, r-release: pblm_0.1-12.zip, r-oldrel: pblm_0.1-12.zip
macOS binaries:	r-release (arm64): pblm_0.1-12.tgz, r-oldrel (arm64): pblm_0.1-12.tgz, r-release (x86_64): pblm_0.1-12.tgz, r-oldrel (x86_64): pblm_0.1-12.tgz

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