Package: pblm
Type: Package
Title: Bivariate Additive Marginal Logistic Regression via Maximum
        Penalized Likelihood Estimation
Version: 0.1-8
Date: 2017-04-2
Author: Marco Enea, with contributions by Mikis Stasinopoulos and
        Robert Rigby
Maintainer: Marco Enea <marco.enea@unipa.it>
Depends: R (>= 3.2.2), Matrix, lattice, splines, MASS
Imports: methods
Description: Bivariate additive categorical regression for
        moderate-to-small size datasets. Under a multinomial scheme, it
        is possible to fit bivariate models when the two responses are
        nominal, ordinal or mixed nominal/ordinal. Partial proportional
        odds models with (non-)uniform association structure can be
        fitted with the possibility to specify several logit types and
        parametrizations for the marginals and the association,
        including the Dale's model. The association structure can also
        be smoothed using penalty terms of polynomial type. P-splines
        are used in the additive part of the model. Common methods such
        as summary, residuals and predict are available.
License: GPL (>= 2)
LazyData: TRUE
NeedsCompilation: no
Packaged: 2026-07-15 07:35:49 UTC; root
URL: http://github.com/enea/pblm
BugReports: http://github.com/enea/pblm/issues
Repository: https://marcoenea.r-universe.dev
Date/Publication: 2020-02-06 10:32:11 UTC
RemoteUrl: https://github.com/marcoenea/pblm
RemoteRef: HEAD
RemoteSha: 1e77d719ceee6a734f1e1cb9e627a174ac3c81b9
RemoteSubdir: pblm