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:
HCTR_0.1.1.tar.gz
HCTR_0.1.1.zip(r-4.5)HCTR_0.1.1.zip(r-4.4)HCTR_0.1.1.zip(r-4.3)
HCTR_0.1.1.tgz(r-4.4-any)HCTR_0.1.1.tgz(r-4.3-any)
HCTR_0.1.1.tar.gz(r-4.5-noble)HCTR_0.1.1.tar.gz(r-4.4-noble)
HCTR_0.1.1.tgz(r-4.4-emscripten)HCTR_0.1.1.tgz(r-4.3-emscripten)
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:0bcbc0be5d. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 17 2024 |
R-4.5-win | OK | Nov 17 2024 |
R-4.5-linux | OK | Nov 17 2024 |
R-4.4-win | OK | Nov 17 2024 |
R-4.4-mac | OK | Nov 17 2024 |
R-4.3-win | OK | Nov 17 2024 |
R-4.3-mac | OK | Nov 17 2024 |
Exports:bounding.seqest.lambdaest.propfinal.selectionhighdim.ppmpv
Dependencies:codetoolsFMStableforeachglmnetharmonicmeanpiteratorslatticeMASSMatrixncvregrbibutilsRcppRcppEigenRdpackshapesurvival
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bounding Sequence | bounding.seq |
Estimated Lambda | est.lambda |
Proportion Estimation | est.prop |
Final Selection | final.selection |
p-values in high-dimensional linear model | highdim.p |
Multi-split Adaptive Lasso | multi.adlasso |
Multi-split Lasso | multi.lasso |
Multi-split MCP | multi.mcp |
Multi-split SCAD | multi.scad |
Permutation p-values | pmpv |