{
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  "Title": "Bivariate Additive Marginal Logistic Regression via Maximum\nPenalized Likelihood Estimation",
  "Version": "0.1-8",
  "Date": "2017-04-2",
  "Author": "Marco Enea, with contributions by Mikis Stasinopoulos and\nRobert Rigby",
  "Maintainer": "Marco Enea <marco.enea@unipa.it>",
  "Description": "Bivariate additive categorical regression for\nmoderate-to-small size datasets. Under a multinomial scheme, it\nis possible to fit bivariate models when the two responses are\nnominal, ordinal or mixed nominal/ordinal. Partial proportional\nodds models with (non-)uniform association structure can be\nfitted with the possibility to specify several logit types and\nparametrizations for the marginals and the association,\nincluding the Dale's model. The association structure can also\nbe smoothed using penalty terms of polynomial type. P-splines\nare used in the additive part of the model. Common methods such\nas summary, residuals and predict are available.",
  "License": "GPL (>= 2)",
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  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "BugReports": "http://github.com/enea/pblm/issues",
  "Repository": "https://marcoenea.r-universe.dev",
  "Date/Publication": "2020-02-06 10:32:11 UTC",
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