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  "Title": "Linear Predictive Models Based on the LIBLINEAR C/C++ Library",
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  "Date": "2024-09-13",
  "Authors@R": "c(\nperson(\"Thibault\",\"Helleputte\",email=\"thibault.helleputte@dnalytics.com\",role=c(\"cre\",\"aut\",\"cph\")),\nperson(\"Jérôme\",\"Paul\",role=c(\"aut\")),\nperson(\"Pierre\",\"Gramme\",role=c(\"aut\")) )",
  "Description": "A wrapper around the LIBLINEAR C/C++ library for machine\nlearning (available at\n<https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is\na simple library for solving large-scale regularized linear\nclassification and regression. It currently supports\nL2-regularized classification (such as logistic regression,\nL2-loss linear SVM and L1-loss linear SVM) as well as\nL1-regularized classification (such as L2-loss linear SVM and\nlogistic regression) and L2-regularized support vector\nregression (with L1- or L2-loss). The main features of\nLiblineaR include multi-class classification (one-vs-the rest,\nand Crammer & Singer method), cross validation for model\nselection, probability estimates (logistic regression only) or\nweights for unbalanced data. The estimation of the models is\nparticularly fast as compared to other libraries.",
  "License": "GPL-2",
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  "URL": "<https://dnalytics.com/software/liblinear/>",
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  "Packaged": {
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    "User": "root"
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  "Repository": "https://dnalytics.r-universe.dev",
  "Date/Publication": "2024-09-14 02:52:13 UTC",
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