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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "EBPRS" in publications use:'
type: software
license: GPL-3.0-only
title: 'EBPRS: Derive Polygenic Risk Score Based on Emprical Bayes Theory'
version: 2.1.0
doi: 10.32614/CRAN.package.EBPRS
abstract: EB-PRS is a novel method that leverages information for effect sizes across
  all the markers to improve the prediction accuracy. No parameter tuning is needed
  in the method, and no external information is needed. This R-package provides the
  calculation of polygenic risk scores from the given training summary statistics
  and testing data. We can use EB-PRS to extract main information, estimate Empirical
  Bayes parameters, derive polygenic risk scores for each individual in testing data,
  and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020)
  <https://doi.org/10.1371/journal.pcbi.1007565> for a detailed presentation of the
  method.
authors:
- family-names: Song
  given-names: Shuang
  email: song-s19@mails.tsinghua.edu.cn
- family-names: Jiang
  given-names: Wei
- family-names: Hou
  given-names: Lin
- family-names: Zhao
  given-names: Hongyu
repository: https://cranhaven.r-universe.dev
commit: 443f8d554e15c5764f34784463efb9d776d059af
date-released: '2026-05-14'
contact:
- family-names: Song
  given-names: Shuang
  email: song-s19@mails.tsinghua.edu.cn