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  "Title": "A Deep Learning for Statistical Classification and Regression\nAnalysis with Random Effects",
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  "Date": "2025-12-15",
  "Authors@R": "person(given = \"Jiwoong\",\nfamily = \"Kim\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jwboys26@gmail.com\")",
  "Description": "Statistical classification and regression have been\npopular among various fields and stayed in the limelight of\nscientists of those fields. Examples of the fields include\nclinical trials where the statistical classification of\npatients is indispensable to predict the clinical courses of\ndiseases. Considering the negative impact of diseases on\nperforming daily tasks, correctly classifying patients based on\nthe clinical information is vital in that we need to identify\npatients of the high-risk group to develop a severe state and\narrange medical treatment for them at an opportune moment. Deep\nlearning - a part of artificial intelligence - has gained much\nattention, and research on it burgeons during past decades:\nsee, e.g, Kazemi and Mirroshandel (2018)\n<DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique\nwhich was originally designed for the classification, and\nhence, the Buddle package can provide sublime solutions to\nvarious challenging classification and regression problems\nencountered in the clinical trials. The Buddle package is based\non the back-propagation algorithm - together with various\npowerful techniques such as batch normalization and dropout -\nwhich performs a multi-layer feed-forward neural network: see\nKrizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber\n(2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al.\n(1998) <DOI:10.1109/5.726791> for more details. This package\ncontains two main functions: TrainBuddle() and FetchBuddle().\nTrainBuddle() builds a feed-forward neural network model and\ntrains the model. FetchBuddle() recalls the trained model which\nis the output of TrainBuddle(), classifies or regresses given\ndata, and make a final prediction for the data.",
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  "Author": "Jiwoong Kim [aut, cre]",
  "Maintainer": "Jiwoong Kim <jwboys26@gmail.com>",
  "Repository": "https://jwboys26.r-universe.dev",
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