{ "_id": "6a1fe08bb401979e7343970a", "Package": "sensR", "Type": "Package", "Title": "Thurstonian Models for Sensory Discrimination", "Version": "1.5-3", "Date": "2023-10-20", "Authors@R": "c(person(given = \"Rune Haubo Bojesen\", family = \"Christensen\",\nemail = \"Rune@ChristensenStatistics.dk\", role = \"aut\"),\nperson(given = \"Per Bruun\", family = \"Brockhoff\",\nemail = \"perbb@dtu.dk\", role = \"aut\"),\nperson(given = \"Alexandra\", family = \"Kuznetsova\", role = \"ctb\"),\nperson(given = \"Sophie\", family = \"Birot\", role = \"ctb\"),\nperson(given = \"Karolina Amelia\", family = \"Stachlewska\", role = \"ctb\"),\nperson(given = \"Dominik\", family = \"Rafacz\", role = \"cre\", email = \"dominik.rafacz@aigora.com\"))", "ByteCompile": "yes", "Description": "Provides methods for sensory discrimination methods;\nduotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A,\nsame-different, 2-AC and degree-of-difference. This enables the\ncalculation of d-primes, standard errors of d-primes, sample\nsize and power computations, and comparisons of different\nd-primes. Methods for profile likelihood confidence intervals\nand plotting are included. Most methods are described in\nBrockhoff, P.B. and Christensen, R.H.B. (2010)\n.", "License": "GPL-2 | GPL-3", "URL": "https://github.com/aigorahub/sensR", "BugReports": "https://github.com/aigorahub/sensR/issues", "Repository": "https://aigorahub.r-universe.dev", "Date/Publication": "2026-02-28 22:35:23 UTC", "RemoteUrl": "https://github.com/aigorahub/sensr", "RemoteRef": "HEAD", "RemoteSha": "65953a1a3eba04e668438641aac310dddae835b0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 08:02:18 UTC", "User": "root" }, "Author": "Rune Haubo Bojesen Christensen [aut],\nPer Bruun Brockhoff [aut],\nAlexandra Kuznetsova [ctb],\nSophie Birot [ctb],\nKarolina Amelia Stachlewska [ctb],\nDominik Rafacz [cre]", "Maintainer": "Dominik Rafacz ", "MD5sum": "2adf1430c1b197c366147d03385520b3", "_user": "aigorahub", "_type": "src", "_file": "sensR_1.5-3.tar.gz", "_fileid": "341dff1d010e8052a6bf20b2540de01a559261e1160431dc8332de481b8aafb1", "_filesize": 1043762, "_sha256": "341dff1d010e8052a6bf20b2540de01a559261e1160431dc8332de481b8aafb1", "_created": 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"discrimSim", "title": "Simulates replicated difference tests", "topics": [ "discrimSim" ] }, { "page": "discrimSS", "title": "Sensory discrimination sample size calculation", "topics": [ "d.primeSS", "discrimSS" ] }, { "page": "dod", "title": "Thurstonian Degree-of-Difference (DOD) model", "topics": [ "dod", "print.dod" ] }, { "page": "dod_fit", "title": "Direct fitter of the Thurstonian Degree-of-Difference (DOD) model", "topics": [ "dod_fit" ] }, { "page": "dod_utils", "title": "Utility functions for the Degree-of-Difference model", "topics": [ "dod_nll", "dod_null", "dod_null_tau", "optimal_tau", "par2prob_dod" ] }, { "page": "dodControl", "title": "Control settings for the dod function", "topics": [ "dodControl" ] }, { "page": "dodPwr", "title": "Power of the Degree-of-Difference (DOD) method", "topics": [ "dodPwr" ] }, { "page": "dodSim", "title": "Simulate data from the Degree-of-Difference model", "topics": [ "dodSim" ] }, { "page": "dprime_compare", "title": "Test the 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