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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1-12 |
rolling linux/jammy R-4.5 | pblm_0.1-12.tar.gz |
331.5 KiB |
0.1-12 |
rolling linux/noble R-4.5 | pblm_0.1-12.tar.gz |
331.4 KiB |
0.1-12 |
rolling source/ R- | pblm_0.1-12.tar.gz |
51.5 KiB |
0.1-12 |
latest linux/jammy R-4.5 | pblm_0.1-12.tar.gz |
331.5 KiB |
0.1-12 |
latest linux/noble R-4.5 | pblm_0.1-12.tar.gz |
331.4 KiB |
0.1-12 |
latest source/ R- | pblm_0.1-12.tar.gz |
51.5 KiB |
0.1-12 |
2026-04-26 source/ R- | pblm_0.1-12.tar.gz |
51.5 KiB |
0.1-12 |
2026-04-23 source/ R- | pblm_0.1-12.tar.gz |
51.5 KiB |
0.1-12 |
2026-04-09 windows/windows R-4.5 | pblm_0.1-12.zip |
334.4 KiB |