Package: RJafroc
Type: Package
Title: Analyzing Diagnostic Observer Performance Studies
Version: 1.3.2
Date: 2020-03-06
Authors@R: c(person("Dev", "Chakraborty", role = c("cre","aut","cph"), email = "dpc10ster@gmail.com"),
             person("Peter", "Philips", role = c("aut"), email = "peter.phillips@cumbria.ac.uk"),
             person("Xuetong", "Zhai", role = c("aut"), email = "xuetong.zhai@gmail.com"),
             person("Lucy","D'Agostino McGowan", role = c("ctb"), email = "ld.mcgowan@vanderbilt.edu"),
             person("Alejandro","RodriguezRuiz", role = c("ctb"), email = "Alejandro.RodriguezRuiz@radboudumc.nl"))
Depends: R (>= 3.5.0)
Imports: bbmle, binom, dplyr, ggplot2, mvtnorm, numDeriv, openxlsx,
        Rcpp, stats, stringr, tools, utils
Suggests: rmarkdown
LinkingTo: Rcpp
Description: Implements software for assessing medical imaging systems, radiologists or computer aided detection algorithms. 
    Models of observer performance are implemented, including the binormal model (BM), the contaminated binormal model (CBM), the 
    correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). The software and applications are 
    described in a book - Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and 
    Applications with R-Based Examples. Taylor-Francis LLC; 2017 - and its vignettes <https://dpc10ster.github.io/RJafroc/>. 
    Observer performance data collection paradigms are the receiver operating characteristic (ROC) and its location specific 
    extensions, primarily free-response ROC (FROC) and the location ROC (LROC). ROC data consists of single ratings per images. 
    A rating is the perceived confidence level that the image is that 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 real lesion. LROC data consists of a rating and a forced 
    localization of the most suspicious region on every image. RJafroc supersedes the Windows version of JAFROC software 
    V4.2.1, <http://www.devchakraborty.com>:. 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, 
    functions for visualizing empirical operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC 
    (wAFROC) curves. Four maximum likelihood curve-fitting algorithms 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 cross the chance diagonal. RSM fitting additionally 
    yields measures of search and lesion-classification performances. Search performance is the ability to find lesions while 
    avoiding finding non-lesions. Lesion-classification performance is the ability to correctly classify found lesions from found 
    non-lesions. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is 
    implemented via both Dorfman-Berbaum-Metz and the Obuchowski-Rockette methods, including Hillis' extensions. Also implemented are 
    single treatment analyses, which allow comparison of performance of a group of radiologists to a specified value, or comparison 
    to CAD to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed 
    treatment factors and the desire is to determined performance in each treatment factor averaged over all levels of the other 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 a 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 international 
    collaborations. This version corrects a few bugs noticed by users and extends the Excel file input format for 
    greater flexibility in handling non-crossed datasets and the sample size routines have been rewritten for ease of use.
License: GPL-3
LazyData: true
URL: https://dpc10ster.github.io/RJafroc/
RoxygenNote: 7.0.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-03-06 18:10:59 UTC; Dev
Author: Dev Chakraborty [cre, aut, cph],
  Peter Philips [aut],
  Xuetong Zhai [aut],
  Lucy D'Agostino McGowan [ctb],
  Alejandro RodriguezRuiz [ctb]
Maintainer: Dev Chakraborty <dpc10ster@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-06 18:40:02 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 13:36:26 UTC; windows
Archs: i386, x64
