{ "_id": "6a1ea448b25058d4daad7c61", "Package": "RJafroc", "Type": "Package", "Title": "Artificial Intelligence Systems and Observer Performance", "Version": "2.1.3", "Date": "2025-04-15", "Authors@R": "c(person('Dev', 'Chakraborty', role = c('cre','aut','cph'), email = 'dpc10ster@gmail.com'),\nperson('Peter', 'Phillips', role = c('ctb'), email = 'peter.phillips@cumbria.ac.uk'),\nperson('Xuetong', 'Zhai', role = c('aut'), email = 'xuetong.zhai@gmail.com'))", "Description": "Analyzing the performance of artificial intelligence (AI)\nsystems/algorithms characterized by a 'search-and-report'\nstrategy. Historically observer performance has dealt with\nmeasuring radiologists' performances in search tasks, e.g.,\nsearching for lesions in medical images and reporting them, but\nthe implicit location information has been ignored. The\nimplemented methods apply to analyzing the absolute and\nrelative performances of AI systems, comparing AI performance\nto a group of human readers or optimizing the reporting\nthreshold of an AI system. In addition to performing historical\nreceiver operating receiver operating characteristic (ROC)\nanalysis (localization information ignored), the software also\nperforms free-response receiver operating characteristic (FROC)\nanalysis, where lesion localization information is used. A book\nusing the software has been published: Chakraborty DP: Observer\nPerformance Methods for Diagnostic Imaging - Foundations,\nModeling, and Applications with R-Based Examples,\nTaylor-Francis LLC; 2017:\n.\nOnline updates to this book, which use the software, are at\n,\n and at\n. Supported data\ncollection paradigms are the ROC, FROC and the location ROC\n(LROC). ROC data consists of single ratings per images, where a\nrating is the perceived confidence level that the image is that\nof a diseased patient. An ROC curve is a plot of true positive\nfraction vs. false positive fraction. FROC data consists of a\nvariable number (zero or more) of mark-rating pairs per image,\nwhere a mark is the location of a reported suspicious region\nand the rating is the confidence level that it is a real\nlesion. LROC data consists of a rating and a location of the\nmost suspicious region, for every image. Four models of\nobserver performance, and curve-fitting software, are\nimplemented: the binormal model (BM), the contaminated binormal\nmodel (CBM), the correlated contaminated binormal model\n(CORCBM), and the radiological search model (RSM). Unlike the\nbinormal model, CBM, CORCBM and RSM predict 'proper' ROC curves\nthat do not inappropriately cross the chance diagonal.\nAdditionally, RSM parameters are related to search performance\n(not measured in conventional ROC analysis) and classification\nperformance. Search performance refers to finding lesions,\ni.e., true positives, while simultaneously not finding false\npositive locations. Classification performance measures the\nability to distinguish between true and false positive\nlocations. Knowing these separate performances allows\nprincipled optimization of reader or AI system performance.\nThis package supersedes Windows JAFROC (jackknife alternative\nFROC) software V4.2.1,\n. Package functions\nare organized as follows. Data file related function names are\npreceded by 'Df', curve fitting functions by 'Fit', included\ndata sets by 'dataset', plotting functions by 'Plot',\nsignificance testing functions by 'St', sample size related\nfunctions by 'Ss', data simulation functions by 'Simulate' and\nutility functions by 'Util'. Implemented are figures of merit\n(FOMs) for quantifying performance and functions for\nvisualizing empirical or fitted operating characteristics:\ne.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC\n(wAFROC) curves. For fully crossed study designs significance\ntesting of reader-averaged FOM differences between modalities\nis implemented via either Dorfman-Berbaum-Metz or the\nObuchowski-Rockette methods. Also implemented is single\nmodality analysis, which allows comparison of performance of a\ngroup of radiologists to a specified value, or comparison of AI\nto a group of radiologists interpreting the same cases.\nCrossed-modality analysis is implemented wherein there are two\ncrossed modality factors and the aim is to determined\nperformance in each modality factor averaged over all levels of\nthe second factor. Sample size estimation tools are provided\nfor ROC and FROC studies; these use estimates of the relevant\nvariances from a pilot study to predict required numbers of\nreaders and cases in a pivotal study to achieve the desired\npower. Utility and data file manipulation functions allow data\nto be read in any of the currently used input formats,\nincluding Excel, and the results of the analysis can be viewed\nin text or Excel output files. The methods are illustrated with\nseveral included datasets from the author's collaborations.\nThis update includes improvements to the code, some as a result\nof user-reported bugs and new feature requests, and others\ndiscovered during ongoing testing and code simplification.", "License": "GPL-3", "LazyData": "true", "URL": "https://dpc10ster.github.io/RJafroc/", "RoxygenNote": "7.3.2", "Encoding": "UTF-8", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://dpc10ster.r-universe.dev", "Date/Publication": "2025-04-20 02:09:17 UTC", "RemoteUrl": "https://github.com/dpc10ster/rjafroc", "RemoteRef": "HEAD", "RemoteSha": "dc33398cbcbe546c426b8175fdb9a230c712d734", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-02 09:25:58 UTC", "User": "root" }, "Author": "Dev Chakraborty [cre, aut, cph],\nPeter Phillips [ctb],\nXuetong Zhai [aut]", "Maintainer": "Dev Chakraborty ", "MD5sum": "d186597923729813722a2606d7e32439", "_user": "dpc10ster", "_type": "src", "_file": "RJafroc_2.1.3.tar.gz", "_fileid": "aa9a9a0d571095db95bd4a32dfff03b5aae97a2d80ba9ce492e3f306d214a419", "_filesize": 5478703, "_sha256": "aa9a9a0d571095db95bd4a32dfff03b5aae97a2d80ba9ce492e3f306d214a419", "_created": "2026-06-02T09:25:58.000Z", "_published": "2026-06-02T09:37:12.531Z", "_distro": "noble", "_jobs": [ { "job": 79040250098, "time": 265, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7354022304" }, { "job": 79040250436, "time": 258, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7354019008" }, { "job": 79040250230, "time": 246, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7354015498" }, { "job": 79040250147, "time": 246, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7354014484" }, { "job": 79040250235, "time": 299, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7354020851" }, { "job": 79040250348, "time": 477, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7354089403" }, { "job": 79040250139, "time": 212, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7353994859" }, { "job": 79040250203, "time": 607, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7354134835" }, { "job": 79039603109, "time": 221, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7353926285" }, { "job": 79040250187, "time": 137, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7353975591" }, { "job": 79040250412, "time": 261, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7354019909" }, { "job": 79040250099, "time": 287, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7354029423" }, { "job": 79040250107, "time": 293, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7354030929" } ], "_buildurl": "https://github.com/r-universe/dpc10ster/actions/runs/26810679192", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/dpc10ster/rjafroc", "_commit": { "id": "dc33398cbcbe546c426b8175fdb9a230c712d734", "author": "dpc10ster ", "committer": "dpc10ster ", "message": "finish up lroc forced rating check\n", "time": 1745114957 }, "_maintainer": { "name": "Dev Chakraborty", "email": "dpc10ster@gmail.com", "login": "dpc10ster", "description": "Dev P. 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