{
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  "Package": "baygel",
  "Title": "Bayesian Shrinkage Estimators for Precision Matrices in Gaussian\nGraphical Models",
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  "Date": "2023-11-11",
  "Authors@R": "c(person(\"Jarod\", \"Smith\", email = \"jarodsmith706@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4235-6147\")),\nperson(\"Mohammad\", \"Arashi\", email = \"arashi@um.ac.ir\", role = \"aut\", comment = c(ORCID = \"0000-0002-5881-9241\")),\nperson(\"Andriette\", \"Bekker\", email = \"andriette.bekker@up.ac.za\", role = \"aut\", comment = c(ORCID = \"0000-0003-4793-5674\")))",
  "Description": "This R package offers block Gibbs samplers for the\nBayesian (adaptive) graphical lasso, ridge, and naive elastic\nnet priors. These samplers facilitate the simulation of the\nposterior distribution of precision matrices for Gaussian\ndistributed data and were originally proposed by: Wang (2012)\n<doi:10.1214/12-BA729>; Smith et al. (2022)\n<doi:10.48550/arXiv.2210.16290> and Smith et al. (2023)\n<doi:10.48550/arXiv.2306.14199>, respectively.",
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  "URL": "https://github.com/Jarod-Smithy/baygel",
  "Repository": "https://jarod-smithy.r-universe.dev",
  "Date/Publication": "2023-11-12 08:02:24 UTC",
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