Tools for quantitative assessment of medical imaging systems, radiologists or computer aided ('CAD') algorithms. 
    Implements methods described in a book: 'Chakraborty' 'DP' (2017), "Observer Performance Methods for Diagnostic Imaging - 
    Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840> 
    and its Online Appendices <https://github.com/dpc10ster/onlinebookk21778>. Data collection paradigms include receiver 
    operating characteristic ('ROC') and a location specific extension, namely free-response 'ROC' ('FROC'). 
    'ROC' data consists of a single rating per image, where the rating is the perceived confidence level the image is of a diseased patient. 
    'FROC' data consists of a variable number (including zero) of mark-rating pairs per image, where a mark is the location of a clinically 
    reportable suspicious region and the rating is the corresponding confidence level that it is a true lesion. The software supersedes 
    the current Windows version of 'JAFROC' software <http://www.devchakraborty.com> which is no longer supported. 
    'RJafroc' is derived from it being an enhanced R version of original Windows 'JAFROC'. Implemented are a number of figures of merit 
    quantifying performance, functions for visualizing operating characteristics; three ROC ratings data curve-fitting algorithms: 
    the 'binormal' model ('BM'), the contaminated binormal model ('CBM') and the radiological search model ('RSM'). Also implemented is 
    maximum likelihood fitting of paired ROC data utilizing the correlated 'CBM' model ('CORCBM'). Unlike the 'BM', 'CBM', 'CORCBM'    
    and the 'RSM' predict proper ROC curves that do not cross the chance diagonal or display inappropriate hooks, usually near the upper 
    right corner of the plots. 'RSM' fitting yields measures of search and lesion-classification performances, in addition to the usual 
    case-classification performance measured by the area under the 'ROC' curve. Search performance is the ability to find lesions while 
    avoiding finding non-lesions. Lesion-classification performance is the ability to discriminate between found lesions and non-lesions. 
    For fully crossed study designs, termed multiple-reader multiple-case, significance testing of reader-averaged figure-of-merit 
    differences between modalities is implemented via both 'Dorfman', 'Berbaum' and 'Metz' ('DBM') and the 'Obuchowski' and 'Rockette' 
    ('OR') methods, both substantially improved by 'Hillis'. Single treatment analysis allows comparison of performance of a group of 
    radiologists to a specified value, or comparison of 'CAD' performance to a group of radiologists interpreting the same cases. Sample 
    size estimation tools are provided for 'ROC' studies that allow estimation of relevant variances from a pilot study to predict required 
    numbers of readers and cases in a pivotal study. 'FROC' sample size estimation is implemented in Online Appendix Chapter 19 available 
    at <https://github.com/dpc10ster/onlinebookk21778>. 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.