Package: HCTR 0.1.1

HCTR: Higher Criticism Tuned Regression

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) <doi: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) <doi: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:Tao Jiang [aut, cre]

HCTR_0.1.1.tar.gz
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HCTR.pdf |HCTR.html
HCTR/json (API)

# Install 'HCTR' in R:
install.packages('HCTR', repos = c('https://lethargy608.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 123 downloads 6 exports 16 dependencies

Last updated 5 years agofrom:0bcbc0be5d. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 18 2024
R-4.5-winOKOct 18 2024
R-4.5-linuxOKOct 18 2024
R-4.4-winOKOct 18 2024
R-4.4-macOKOct 18 2024
R-4.3-winOKOct 18 2024
R-4.3-macOKOct 18 2024

Exports:bounding.seqest.lambdaest.propfinal.selectionhighdim.ppmpv

Dependencies:codetoolsFMStableforeachglmnetharmonicmeanpiteratorslatticeMASSMatrixncvregrbibutilsRcppRcppEigenRdpackshapesurvival