Package: bcROCsurface 1.0-6

bcROCsurface: Bias-Corrected Methods for Estimating the ROC Surface of Continuous Diagnostic Tests

The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption.

Authors:Duc-Khanh To, with contributions from Monica Chiogna and Gianfranco Adimari

# Install 'bcROCsurface' in R:

install.packages('bcROCsurface', repos = c('https://toduckhanh.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/toduckhanh/bcrocsurface/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

EOC - A sub-data from Pre-PLCO Phase II Dataset

On CRAN:

openblas cpp

3.51 score 16 scripts 370 downloads 2 mentions 10 exports 32 dependencies

Last updated from:55c86f0478. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	223
linux-devel-x86_64	OK	172
source / vignettes	OK	227
linux-release-arm64	OK	185
linux-release-x86_64	OK	194
macos-release-arm64	OK	186
macos-release-x86_64	OK	231
macos-oldrel-arm64	OK	196
macos-oldrel-x86_64	OK	328
windows-devel	OK	116
windows-release	OK	151
windows-oldrel	OK	145
wasm-release	OK	187

Exports:asy_cov_tcf asy_var_vus cv_knn pre_data psglm rho_knn rho_mlogit rocs rocs.tcf vus_mar

Dependencies:base64enc boot bslib cachem cli digest evaluate fastmap fontawesome fs highr htmltools htmlwidgets jquerylib jsonlite knitr lifecycle magrittr memoise mime nnet R6 rappdirs Rcpp RcppArmadillo rgl rlang rmarkdown sass tinytex xfun yaml

Guide for use bcROCsurface

Rendered fromexample.Rmdusingknitr::rmarkdown

Last update: 2023-09-09
Started: 2016-10-10

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bias-Corrected Methods for Estimating the ROC Surface of Continuous Diagnostic Tests	bcROCsurface-package bcROCsurface
Asymptotic variance-covariance estimation for True Class Fractions (TCFs) at the cut point (c_1, c_2)	asy_cov_tcf
Asymptotic variance estimation for VUS	asy_var_vus
Cross-validation for K nearest-neighbor regression	cv_knn
A sub-data from Pre-PLCO Phase II Dataset	EOC
Preparing monotone ordered disease classes	pre_data
Print summary results of VUS	print.vus_mar
Fitting verification models	psglm
K nearest-neighbor (KNN) regression	rho_knn
Fitting disease models via multinomial logistic models	rho_mlogit
Receiver operating characteristics surface for a continuous diagnostic test	rocs rocs.tcf ROCsurface
Estimation methods for volume under ROC surface (VUS) under MAR	vus_mar