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  "Package": "automatedRecLin",
  "Title": "Record Linkage Based on an Entropy-Maximizing Classifier",
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  "Authors@R": "c(person(given = \"Adam\",\nfamily = \"Struzik\",\nrole = c(\"aut\", \"cre\"),\nemail = \"adastr5@st.amu.edu.pl\",\ncomment = c(ORCID = \"0009-0002-2547-482X\")),\nperson(given = \"Maciej\",\nfamily = \"Beręsewicz\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"maciej.beresewicz@ue.poznan.pl\",\ncomment = c(ORCID = \"0000-0002-8281-4301\")))",
  "Description": "The goal of 'automatedRecLin' is to perform record linkage\n(also known as entity resolution) in unsupervised or supervised\nsettings. It compares pairs of records from two datasets using\nselected comparison functions to estimate the probability or\ndensity ratio between matched and non-matched records. Based on\nthese estimates, it predicts a set of matches that maximizes\nentropy. For details see: Lee et al. (2022)\n<https://www150.statcan.gc.ca/n1/pub/12-001-x/2022001/article/00007-eng.htm>,\nVo et al. (2023)\n<https://ideas.repec.org/a/eee/csdana/v179y2023ics0167947322002365.html>,\nSugiyama et al. (2008) <doi:10.1007/s10463-008-0197-x>.",
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  "Date/Publication": "2026-06-01 10:40:52 UTC",
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