bandsfdp: Compute Upper Prediction Bounds on the FDP in Competition-Based Setups

Implements functions that calculate upper prediction bounds on the false discovery proportion (FDP) in the list of discoveries returned by competition-based setups, implementing Ebadi et al. (2022) <arXiv:2302.11837>. Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression (note this package typically uses the terminology of TDC). Included is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the simultaneous standardized and uniform bands. Requires pre-computed Monte Carlo statistics available at <https://github.com/uni-Arya/fdpbandsdata>. This data can be downloaded by running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R and restarting R after installation. The size of this data is roughly 81Mb.

Getting started

Package details

AuthorArya Ebadi [aut, cre], Dong Luo [aut], Jack Freestone [aut], William Stafford Noble [aut], Uri Keich [aut] (<https://orcid.org/0000-0002-3209-5011>)
MaintainerArya Ebadi <aeba3842@uni.sydney.edu.au>
LicenseMIT + file LICENSE
Version1.1.0
URL https://github.com/uni-Arya/bandsfdp
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("bandsfdp")

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bandsfdp documentation built on May 31, 2023, 7:24 p.m.