CRAN/E | BayesianFROC

BayesianFROC

FROC Analysis by Bayesian Approaches

Installation

About

Provides new methods for the so-called Free-response Receiver Operating Characteristic (FROC) analysis. The ultimate aim of FROC analysis is to compare observer performances, which means comparing characteristics, such as area under the curve (AUC) or figure of merit (FOM). In this package, we only use the notion of AUC for modality comparison, where by "modality", we mean imaging methods such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ..., etc. So there is a problem that which imaging method is better to detect lesions from shadows in radiographs. To solve modality comparison issues, this package provides new methods using hierarchical Bayesian models proposed by the author of this package. Using this package, one can obtain at least one conclusion that which imaging methods are better for finding lesions in radiographs with the case of your data. Fitting FROC statistical models is sometimes not so good, it can easily confirm by drawing FROC curves and comparing these curves and the points constructed by False Positive fractions (FPFs) and True Positive Fractions (TPFs), we can validate the goodness of fit intuitively. Such validation is also implemented by the Chi square goodness of fit statistics in the Bayesian context which means that the parameter is not deterministic, thus by integrating it with the posterior predictive measure, we get a desired value. To compare modalities (imaging methods: MRI, CT, PET, ... , etc), we evaluate AUCs for each modality. FROC is developed by Dev Chakraborty, his FROC model in his 1989 paper relies on the maximal likelihood methodology. The author modified and provided the alternative Bayesian FROC model. Strictly speaking, his model does not coincide with models in this package. In FROC context, we means by multiple reader and multiple case (MRMC) the case of the number of reader or modality is two or more. The MRMC data is available for functions of this package. I hope that medical researchers use not only the frequentist method but also alternative Bayesian methods. In medical research, many problems are considered under only frequentist methods, such as the notion of p-values. But p-value is sometimes misunderstood. Bayesian methods provide very simple, direct, intuitive answer for research questions. Combining frequentist methods with Bayesian methods, we can obtain more reliable answer for research questions. References: Dev Chakraborty (1989) Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data.

Citation BayesianFROC citation info

Key Metrics

Version 1.0.0
R ≥ 3.5.0
Published 2022-01-23 795 days ago
Needs compilation? yes
License MIT
License File
CRAN checks BayesianFROC results

Downloads

Yesterday 1 0%
Last 7 days 47 +24%
Last 30 days 190 +13%
Last 90 days 558 +47%
Last 365 days 1.951 -54%

Maintainer

Maintainer

Issei Tsunoda

tsunoda.issei1111@gmail.com

Authors

Issei Tsunoda

aut / cre

Material

README
NEWS
Reference manual
Package source

In Views

Bayesian

Vignettes

FROC Models
Reference

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

BayesianFROC archive

Depends

rstan ≥ 2.18.2
R ≥ 3.5.0
Rcpp

Imports

knitr
stats
graphics
tcltk
grDevices
ggplot2
methods
car
crayon
bridgesampling
rhandsontable
shiny
pracma
shinydashboard
shinythemes
fastDummies
shinyjs

Suggests

hexbin
MASS
magrittr
markdown
rmarkdown