{ "_id": "6ae48012327abcdf86868626", "Package": "bmetapred", "Title": "Bayesian Meta-Prediction in the Presence of Prediction Error", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Jorge\", \"Piqueras Marqués\", , \"jorge.piqueras.m@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6812-2106\")),\nperson(\"Anabel\", \"Forte Deltell\", , \"anabel.forte@uv.es\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-9534-1817\")),\nperson(\"Joaquin\", \"Martinez Minaya\", , \"jmarmin@eio.upv.es\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-1016-8734\"))\n)", "Description": "Fits pseudo-Bayesian Model Averaging (pBMA) and Bayesian\nStacking ensembles of linear meta-models while explicitly\naccounting for the prediction errors of the individual models,\nbuilding on the Bayesian Stacking of predictive distributions\nframework of Yao et al. (2018) . When\nnative uncertainty estimates are unavailable, it provides tools\nto estimate heteroscedastic errors via Local Polynomial\nRegression (LOESS). The meta-model space is explored using\nexhaustive search, a Markov Chain Monte Carlo Model Composition\n(MC3) algorithm following Madigan and Raftery (1994)\n when pBMA is selected, or\na tabu-guided heuristic search with parallel tempering when\nbayesian stacking is selected, all powered by 'brms' (Bürkner,\n2017 ).", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "8.0.0", "Config/pak/sysreqs": "cmake make libicu-dev", "Repository": "https://jorgepiquerasmarques.r-universe.dev", "Date/Publication": "2026-10-29 10:20:14 UTC", "RemoteUrl": "https://github.com/jorgepiquerasmarques/bmetapred", "RemoteRef": "HEAD", "RemoteSha": "05e6001147cedc3fcaccd679c20f986082c1cf6e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-30 11:27:33 UTC", "User": "root" }, "Author": "Jorge Piqueras Marqués [aut, cre] (ORCID:\n),\nAnabel Forte Deltell [aut] (ORCID:\n),\nJoaquin Martinez Minaya [ctb] (ORCID:\n)", "Maintainer": "Jorge Piqueras Marqués ", "_user": "jorgepiquerasmarques", "_type": "src", "_file": "bmetapred_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/81b9640322de0096ada9ddf5351ac27b60f921f592c6cab9302ebf24623c7685", "_filesize": 119965, "_sha256": "81b9640322de0096ada9ddf5351ac27b60f921f592c6cab9302ebf24623c7685", "_expires": "2027-02-07T11:31:28.000Z", "_created": "2026-10-30T11:27:33.000Z", "_published": "2026-10-30T11:31:30.537Z", "_upstream": "https://github.com/jorgepiquerasmarques/bmetapred", "_status": "success", "_maintainer": { "name": "Jorge Piqueras Marqués", "email": "jorge.piqueras.m@gmail.com", "login": "jorgepiquerasmarques", "orcid": "0000-0002-6812-2106", "description": "Machine learning methods. Bayesian statistics.\nEpidemiology.", "uuid": 219674070 }, "_jobs": [ { "job": 124037632902, "time": 132, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12973552167" }, { "job": 124037632877, "time": 174, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12973367397" }, { "job": 124037632810, "time": 205, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "12972833663" }, { "job": 124037633977, "time": 167, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12972654798" }, { "job": 124036374160, "time": 187, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12973127370" }, { "job": 124037632888, "time": 145, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12974090553" }, { "job": 124037632945, "time": 98, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12972484079" }, { "job": 124037633025, "time": 85, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12973257872" }, { "job": 124037632910, "time": 78, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12972664746" } ], "_buildurl": "https://github.com/r-universe/jorgepiquerasmarques/actions/runs/41047863150", "_commit": { "id": "05e6001147cedc3fcaccd679c20f986082c1cf6e", "author": "Jorge Piqueras-Marqués ", "committer": "GitHub ", "message": "Add files via upload", "time": 1793269214 }, "_distro": "resolute", "_host": "GitHub-Actions", "_registered": true, "_dependencies": [ { "package": "brms", "role": "Imports" }, { "package": "future", "role": "Imports" }, { "package": "future.apply", "role": "Imports" }, { "package": "loo", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" } ], "_owner": "jorgepiquerasmarques", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-25", "n": 1 }, { "week": "2026-44", "n": 4 } ], "_tags": [], "_topics": [ "bayesian-methods", "bayesian-model-averaging", "markov-chain-monte-carlo", "meta-model", "meta-predictor", "prediction-error", "stacking-ensemble" ], "_stars": 0, "_contributors": [ { "user": "jorgepiquerasmarques", "count": 4, "uuid": 219674070 } ], "_userbio": { "uuid": 219674070, "type": "user", "name": "Jorge Piqueras-Marqués", "followers": 0, "description": "Machine learning methods. 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