Tools for quantitative assessment of medical imaging systems, radiologists 
    or computer aided detection ('CAD') algorithms. Implements methods described in the book: 
    'Chakraborty' (2017) <ISBN:978-1482214840>. 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 relevant suspicious region and the rating is the corresponding 
    confidence level that it is a true lesion. The name 'RJafroc' is derived from it being an 
    enhanced R version of original Windows 'JAFROC' <http://www.devchakraborty.com>. Implemented 
    are a number of figures of merit quantifying performance, functions for visualizing 
    operating characteristics and three ROC ratings data curve-fitting algorithms: the 'binormal' 
    model ('BM'), the contaminated 'binormal' model ('CBM') and the 'radiological' search model 
    ('RSM') 'Chakraborty' (2006) <{doi:10.1088/0031-9155/51/14/012}> . Also implemented is maximum 
    likelihood fitting of paired ROC data, utilizing the correlated 'CBM' model ('CORCBM') model. 
    Unlike the 'BM', which predicts 'improper' ROC curves, 'CBM', 'CORCBM' and the 'RSM' predict 
    proper ROC curves that do not cross the chance diagonal. '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. A number of significance testing algorithms 
    are implement. For fully-crossed factorial study designs, termed multiple-reader multiple-case, 
    significance testing of reader-averaged figure-of-merit differences between 'modalities' is 
    implemented using either 'pseudovalue'-based or figure of merit-based methods. 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' and 'FROC' studies that allow estimation of relevant variances 
    from a pilot study, in order to predict required numbers of readers and cases in a pivotal study. 
    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.