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  "Title": "Penalized Ordinal Regression",
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  "Authors@R": "c(\nperson(\"Michael\", \"Wurm\", email = \"wurm@uwalumni.com\", role = c(\"aut\", \"cre\")),\nperson(\"Paul\", \"Rathouz\", email = \"rathouz@biostat.wisc.edu\", role = \"aut\"),\nperson(\"Bret\", \"Hanlon\", email = \"hanlon@stat.wisc.edu\", role = \"aut\"))",
  "Description": "Fits ordinal regression models with elastic net penalty.\nSupported model families include cumulative probability,\nstopping ratio, continuation ratio, and adjacent category.\nThese families are a subset of vector glm's which belong to a\nmodel class we call the elementwise link multinomial-ordinal\n(ELMO) class. Each family in this class links a vector of\ncovariates to a vector of class probabilities. Each of these\nfamilies has a parallel form, which is appropriate for ordinal\nresponse data, as well as a nonparallel form that is\nappropriate for an unordered categorical response, or as a more\nflexible model for ordinal data. The parallel model has a\nsingle set of coefficients, whereas the nonparallel model has a\nset of coefficients for each response category except the\nbaseline category. It is also possible to fit a model with both\nparallel and nonparallel terms, which we call the semi-parallel\nmodel. The semi-parallel model has the flexibility of the\nnonparallel model, but the elastic net penalty shrinks it\ntoward the parallel model. For details, refer to Wurm, Hanlon,\nand Rathouz (2021) <doi:10.18637/jss.v099.i06>.",
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      "title": "Ordinal regression models with elastic net penalty",
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      "title": "Uses K-fold cross validation to obtain out-of-sample log-likelihood and misclassification rates. Lambda is tuned within each cross validation fold.",
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