{ "_id": "6ad0b174f55ded6d77dfa516", "Package": "metaggR", "Type": "Package", "Title": "Calculate the Knowledge-Weighted Estimate", "Version": "0.3.0", "Authors@R": "c(person(given = \"Ville\",\nfamily = \"Satopää\",\nemail = \"ville.satopaa@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(given = \"Asa\",\nfamily = \"Palley\",\nrole = \"aut\"))", "Description": "According to a phenomenon known as \"the wisdom of the\ncrowds,\" combining point estimates from multiple judges often\nprovides a more accurate aggregate estimate than using a point\nestimate from a single judge. However, if the judges use shared\ninformation in their estimates, the simple average will\nover-emphasize this common component at the expense of the\njudges’ private information. Asa Palley & Ville Satopää (2021)\n\"Boosting the Wisdom of Crowds Within a Single Judgment\nProblem: Selective Averaging Based on Peer Predictions\"\n\nproposes a procedure for calculating a weighted average of the\njudges’ individual estimates such that resulting aggregate\nestimate appropriately combines the judges' collective\ninformation within a single estimation problem. The authors use\nboth simulation and data from six experimental studies to\nillustrate that the weighting procedure outperforms existing\naveraging-like methods, such as the equally weighted average,\ntrimmed average, and median. This aggregate estimate -- know as\n\"the knowledge-weighted estimate\" -- inputs a) judges'\nestimates of a continuous outcome (E) and b) predictions of\nothers' average estimate of this outcome (P). In this\nR-package, the function knowledge_weighted_estimate(E,P)\nimplements the knowledge-weighted estimate. Its use is\nillustrated with a simple stylized example and on real-world\nexperimental data.", "License": "GPL-2", "Copyright": "(c) Ville Satopaa", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-15 10:53:08 UTC", "User": "root" }, "Author": "Ville Satopää [aut, cre, cph], Asa Palley [aut]", "Maintainer": "Ville Satopää ", "Repository": "https://satopaa.r-universe.dev", "Date/Publication": "2022-04-25 09:00:02 UTC", "RemoteUrl": "https://github.com/cran/metaggR", "RemoteRef": "HEAD", "RemoteSha": "6aec9ebb600f1f5278f3fe8800c530b03ec611f3", "_user": "satopaa", "_type": "src", "_file": "metaggR_0.3.0.tar.gz", "_fileid": "https://r2.ropensci.org/9f771d65c6a3afc7db122388969731aa79b5892730e533cb176df42b1efa33ea", "_filesize": 466487, "_sha256": "9f771d65c6a3afc7db122388969731aa79b5892730e533cb176df42b1efa33ea", "_expires": "2027-01-23T10:56:50.000Z", "_created": "2026-10-15T10:53:08.000Z", "_published": "2026-10-15T10:56:52.715Z", "_buildurl": "https://github.com/r-universe/satopaa/actions/runs/38723057292", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 116383481808, "time": 173, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11967275141" }, { "job": 116383481742, "time": 149, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11966488745" }, { "job": 116383481814, "time": 136, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11966748745" }, { "job": 116383481866, "time": 91, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11966403808" }, { "job": 116382285622, "time": 181, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11966378405" }, { "job": 116383481706, "time": 179, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11965839930" }, { "job": 116383481725, "time": 78, 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"Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_contributors": [ { "user": "satopaa", "count": 3, "uuid": 2185253 } ], "_userbio": { "uuid": 2185253, "type": "user", "name": "Ville Satopaa", "followers": 3 }, "_downloads": { "count": 255, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/metaggR" }, "_searchresults": 15, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/metaggR.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md" ], "_realowner": "satopaa", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2021-01-09" }, { "version": "0.2.0", "date": "2021-08-17" }, { "version": "0.3.0", "date": "2022-04-25" } ], "_exports": [ "get_influence_scores", "knowledge_gap", "knowledge_weighted_estimate", "knowledge_weights" ], "_datasets": [ { "name": "E_CALORIES_FINAL", "title": "Data: Calorie Counts", "object": "E_CALORIES_FINAL", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_CALORIES_INITIAL", "title": "Data: Calorie Counts", "object": "E_CALORIES_INITIAL", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_COINS_NESTED", "title": "Data: Coin Flips", "object": "E_COINS_NESTED", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_COINS_NESTED_SYMMETRIC", "title": "Data: Coin Flips", "object": "E_COINS_NESTED_SYMMETRIC", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_COINS_SYMMETRIC", "title": "Data: Coin Flips", "object": "E_COINS_SYMMETRIC", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_GK_1", "title": "Data: General Knowledge Statements", "object": "E_GK_1", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_GK_2", "title": "Data: General Knowledge Statements", "object": "E_GK_2", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_GK_3", "title": "Data: General Knowledge Statements", "object": "E_GK_3", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_GK_4", "title": "Data: General Knowledge Statements", "object": "E_GK_4", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_GK_5", "title": "Data: General Knowledge Statements", "object": "E_GK_5", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_GROCERIES", "title": "Data: Grocery Prices", "object": "E_GROCERIES", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "E_NCAA_R16", "title": "Data: NCAA Basketball", "object": "E_NCAA_R16", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "E_NCAA_R64", 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"object": "ID_GK_2", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "ID_GK_3", "title": "Data: General Knowledge Statements", "object": "ID_GK_3", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "ID_GK_4", "title": "Data: General Knowledge Statements", "object": "ID_GK_4", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "ID_GK_5", "title": "Data: General Knowledge Statements", "object": "ID_GK_5", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "ID_GROCERIES", "title": "Data: Grocery Prices", "object": "ID_GROCERIES", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "ID_NCAA_R16", "title": "Data: NCAA Basketball", "object": "ID_NCAA_R16", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "ID_NCAA_R64", "title": "Data: NCAA Basketball", "object": "ID_NCAA_R64", "class": [ "list" ], "fields": [], "table": 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"title": "Data: General Knowledge Statements", "object": "P_GK_3", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "P_GK_4", "title": "Data: General Knowledge Statements", "object": "P_GK_4", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "P_GK_5", "title": "Data: General Knowledge Statements", "object": "P_GK_5", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "P_GROCERIES", "title": "Data: Grocery Prices", "object": "P_GROCERIES", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "P_NCAA_R16", "title": "Data: NCAA Basketball", "object": "P_NCAA_R16", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "P_NCAA_R64", "title": "Data: NCAA Basketball", "object": "P_NCAA_R64", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "THETA_CALORIES", "title": "Data: Calorie Counts", "object": "THETA_CALORIES", "class": [ 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"Calorie_Counts", "E_CALORIES_FINAL", "E_CALORIES_INITIAL", "ID_CALORIES", "P_CALORIES", "THETA_CALORIES" ] }, { "page": "Coin_Flips", "title": "Data: Coin Flips", "topics": [ "Coin_Flips", "E_COINS_NESTED", "E_COINS_NESTED_SYMMETRIC", "E_COINS_SYMMETRIC", "ID_COINS_NESTED", "ID_COINS_NESTED_SYMMETRIC", "ID_COINS_SYMMETRIC", "P_COINS_NESTED", "P_COINS_NESTED_SYMMETRIC", "P_COINS_SYMMETRIC", "THETA_COINS_NESTED", "THETA_COINS_NESTED_SYMMETRIC", "THETA_COINS_SYMMETRIC" ] }, { "page": "General_Knowledge_Statements", "title": "Data: General Knowledge Statements", "topics": [ "E_GK_1", "E_GK_2", "E_GK_3", "E_GK_4", "E_GK_5", "General_Knowledge_Statements", "ID_GK_1", "ID_GK_2", "ID_GK_3", "ID_GK_4", "ID_GK_5", "P_GK_1", "P_GK_2", "P_GK_3", "P_GK_4", "P_GK_5", "THETA_GK_1", "THETA_GK_2", "THETA_GK_3", "THETA_GK_4", "THETA_GK_5" ] }, { "page": "get_influence_scores", "title": "Calculate the Influence Scores", "topics": [ "get_influence_scores" ] }, { "page": "Grocery_Prices", "title": "Data: Grocery Prices", "topics": [ "E_GROCERIES", "Grocery_Prices", "ID_GROCERIES", "P_GROCERIES", "THETA_GROCERIES" ] }, { "page": "knowledge_gap", "title": "Calculate the Knowledge Gap", "topics": [ "knowledge_gap" ] }, { "page": "knowledge_weighted_estimate", "title": "Knowledge-Weighted Estimate", "topics": [ "knowledge_weighted_estimate" ] }, { "page": "knowledge_weights", "title": "Calculate the Weights that Minimize the Knowledge Gap", "topics": [ "knowledge_weights" ] }, { "page": "NCAA_Basketball", "title": "Data: NCAA Basketball", "topics": [ "E_NCAA_R16", "E_NCAA_R64", "ID_NCAA_R16", "ID_NCAA_R64", "NCAA_Basketball", "P_NCAA_R16", "P_NCAA_R64", "THETA_NCAA_R16", "THETA_NCAA_R64" ] } ], "_readme": "https://github.com/cran/metaggR/raw/HEAD/README.md", "_rundeps": [ "MASS" ], "_vignettes": [ { "source": "user_manual.Rmd", "filename": "user_manual.html", "title": "Knowledge Weighted Estimate", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Example 1: Three Gorges Dam", 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