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  "Package": "countts",
  "Title": "Thomson Sampling for Zero-Inflated Count Outcomes",
  "Version": "0.1.0",
  "Authors@R": "c(\nperson(\"Xueqing\", \"Liu\", email = \"xueqing_liu@u.duke.nus.edu\", role = \"aut\"),\nperson(\"Nina\", \"Deliu\", email = \"nina.deliu@uniromal.it\", role = \"aut\"),\nperson(\"Tanujit\", \"Chakraborty\", email = \"tanujitisi@gmail.com\", role = c(\"aut\", \"cre\",\"cph\"),\ncomment = c(ORCID = \"0000-0002-3479-2187\")),\nperson(\"Lauren\", \"Bell\", email = \"lauren.bell@mrc-bsu.cam.ac.uk\", role = \"aut\"),\nperson(\"Bibhas\", \"Chakraborty\", email = \"bibhas.chakraborty@duke-nus.edu.sg\", role = \"aut\"))",
  "Description": "A specialized tool is designed for assessing contextual\nbandit algorithms, particularly those aimed at handling\noverdispersed and zero-inflated count data. It offers a\nsimulated testing environment that includes various models like\nPoisson, Overdispersed Poisson, Zero-inflated Poisson, and\nZero-inflated Overdispersed Poisson. The package is capable of\nexecuting five specific algorithms: Linear Thompson sampling\nwith log transformation on the outcome, Thompson sampling\nPoisson, Thompson sampling Negative Binomial, Thompson sampling\nZero-inflated Poisson, and Thompson sampling Zero-inflated\nNegative Binomial. Additionally, it can generate regret plots\nto evaluate the performance of contextual bandit algorithms.\nThis package is based on the algorithms by Liu et al. (2023)\n<arXiv:2311.14359>.",
  "Maintainer": "Tanujit Chakraborty <tanujitisi@gmail.com>",
  "License": "GPL (>= 2)",
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  "Packaged": {
    "Date": "2026-05-12 06:16:12 UTC",
    "User": "root"
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  "Author": "Xueqing Liu [aut], Nina Deliu [aut], Tanujit Chakraborty [aut,\ncre, cph] (<https://orcid.org/0000-0002-3479-2187>), Lauren\nBell [aut], Bibhas Chakraborty [aut]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-11-30 02:39:18 UTC",
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      "title": "Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms",
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      "title": "Apply the algorithms to make decisions for Thompson sampling Negative Binomial (TS-NB) algorithms",
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      "page": "apply_ZIP",
      "title": "Apply the algorithms to make decisions for Thompson sampling Zero-inflated Poisson (TS-ZIP) algorithm",
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      "title": "Summarize the simulation results and generate the regret plot",
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      "title": "Updating parameters in algorithm",
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