Analyzing the performance of artificial intelligence
 (AI) systems/algorithms characterized by a 'search-and-report'
 strategy. Historically observer performance has dealt with 
 measuring radiologists' performances in search tasks, e.g., searching
 for lesions in medical images and reporting them, but the implicit 
 location information has been ignored. The implemented methods apply 
 to analyzing the absolute and relative performances of AI systems, 
 comparing AI performance to a group of human readers or optimizing the
 reporting threshold of an AI system. In addition to performing historical
 receiver operating receiver operating characteristic (ROC) analysis 
 (localization information ignored), the software also performs
 free-response receiver operating characteristic (FROC) 
 analysis, where lesion localization information is used. A book
 using the software has been published: Chakraborty DP: Observer
 Performance Methods for Diagnostic Imaging - Foundations, Modeling,
 and Applications with R-Based Examples, Taylor-Francis LLC; 2017:
 <https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840>.
 Online updates to this book, which use the software, are at
 <https://dpc10ster.github.io/RJafrocQuickStart/>,
 <https://dpc10ster.github.io/RJafrocRocBook/> and at
 <https://dpc10ster.github.io/RJafrocFrocBook/>. Supported data
 collection paradigms are the ROC, FROC and the location ROC (LROC).
 ROC data consists of single ratings per images, where a rating is
 the perceived confidence level that the image is that of a diseased
 patient. An ROC curve is a plot of true positive fraction vs. false
 positive fraction. FROC data consists of a variable number (zero or
 more) of mark-rating pairs per image, where a mark is the location
 of a reported suspicious region and the rating is the confidence
 level that it is a real lesion. LROC data consists of a rating and a
 location of the most suspicious region, for every image. Four models
 of observer performance, and curve-fitting software, are implemented:
 the binormal model (BM), the contaminated binormal model (CBM), the
 correlated contaminated binormal model (CORCBM), and the radiological
 search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM
 predict 'proper' ROC curves that do not inappropriately cross the
 chance diagonal. Additionally, RSM parameters are related to search
 performance (not measured in conventional ROC analysis) and
 classification performance. Search performance refers to finding
 lesions, i.e., true positives, while simultaneously not finding false
 positive locations. Classification performance measures the ability to
 distinguish between true and false positive locations. Knowing these
 separate performances allows principled optimization of reader or AI
 system performance. This package supersedes Windows JAFROC (jackknife
 alternative FROC) software V4.2.1,
 <https://github.com/dpc10ster/WindowsJafroc>. Package functions are
 organized as follows. Data file related function names are preceded
 by 'Df', curve fitting functions by 'Fit', included data sets by 'dataset',
 plotting functions by 'Plot', significance testing functions by 'St',
 sample size related functions by 'Ss', data simulation functions by
 'Simulate' and utility functions by 'Util'. Implemented are figures of
 merit (FOMs) for quantifying performance and functions for visualizing
 empirical or fitted operating characteristics: e.g., ROC, FROC, alternative
 FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study
 designs significance testing of reader-averaged FOM differences between
 modalities is implemented via either Dorfman-Berbaum-Metz or the
 Obuchowski-Rockette methods. Also implemented is single treatment analysis,
 which allows comparison of performance of a group of radiologists to a
 specified value, or comparison of AI to a group of radiologists interpreting
 the same cases. Crossed-modality analysis is implemented wherein there are
 two crossed treatment factors and the aim is to determined performance in
 each treatment factor averaged over all levels of the second factor. Sample
 size estimation tools are provided for ROC and FROC studies; these use
 estimates of the relevant variances from a pilot study to predict required
 numbers of readers and cases in a pivotal study to achieve the desired power.
 Utility and data file manipulation functions allow data to be read in any of
 the currently used input formats, including Excel, and the results of the
 analysis can be viewed in text or Excel output files. The methods are
 illustrated with several included datasets from the author's collaborations.
 This update includes improvements to the code, some as a result of
 user-reported bugs and new feature requests, and others discovered during
 ongoing testing and code simplification.