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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "HCTR" in publications use:'
type: software
license: GPL-2.0-only
title: 'HCTR: Higher Criticism Tuned Regression'
version: 0.1.1
doi: 10.32614/CRAN.package.HCTR
abstract: A novel searching scheme for tuning parameter in high-dimensional penalized
  regression. We propose a new estimate of the regularization parameter based on an
  estimated lower bound of the proportion of false null hypotheses (Meinshausen and
  Rice (2006) <https://doi.org/10.1214/009053605000000741>). The bound is estimated
  by applying the empirical null distribution of the higher criticism statistic, a
  second-level significance testing, which is constructed by dependent p-values from
  a multi-split regression and aggregation method (Jeng, Zhang and Tzeng (2019) <https://doi.org/10.1080/01621459.2018.1518236>).
  An estimate of tuning parameter in penalized regression is decided corresponding
  to the lower bound of the proportion of false null hypotheses. Different penalized
  regression methods are provided in the multi-split algorithm.
authors:
- family-names: Jiang
  given-names: Tao
  email: tjiang8@ncsu.edu
repository: https://cranhaven.r-universe.dev
commit: bd8b0d404744619d02b079ff55f52e319165e8d0
date-released: '2026-05-14'
contact:
- family-names: Jiang
  given-names: Tao
  email: tjiang8@ncsu.edu