Package: itrimhoch 1.0.0

Jiangtao Gou

itrimhoch: Improved Trimmed Weighted Hochberg Procedures and Sample Size Optimization

The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods. See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report.

Authors:Jiangtao Gou [aut, cre], Fengqing Zhang [aut], Yizhuo Chang [ctb], Tianqi Li [ctb]

itrimhoch_1.0.0.tar.gz
itrimhoch_1.0.0.tar.gz(r-4.7-any)itrimhoch_1.0.0.tar.gz(r-4.6-any)
itrimhoch_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
itrimhoch/json (API)

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

On CRAN:

Conda:

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

1.00 score 532 downloads 26 exports 1 dependencies

Last updated from:e01090e6a6. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK96
source / vignettesOK134
linux-release-x86_64OK101
wasm-releaseOK96

Exports:find_k_given_rho_target_mvtnormfind_k_target_mvtnormfind_rho_target_mvtnormiHpTarget1iHpTarget1miHpTarget2iHpTarget2minterpolate_zeroitwcHochPoweroptkoptrhooptsamplesize_iHpoptsamplesize_iHpmoptsamplesize_tHpoptsamplesize_wHolmpmoptsamplesize_wHptHpTarget1tHpTarget2typeIerror_Simes_mvtnormtypeIerror_trimSimes_mvtnormwHolmTarget1wHolmTarget1mwHolmTarget2wHolmTarget2mwHpTarget1wHpTarget2

Dependencies:mvtnorm

Readme and manuals

Help Manual

Help pageTopics
Find the difference between the error rate when k and rho are both given and the prespecified alpha levelfind_k_given_rho_target_mvtnorm
Find the difference between the error rate when k is specified and rho is optimal and the prespecified alpha levelfind_k_target_mvtnorm
Find the partial derivative of the error rate with respect to the correlation coefficient rho when k and rho are givenfind_rho_target_mvtnorm
Find the difference between the achieved power and the desired power for rejecting H1 using the improved trimmed or truncated weighted Hochberg procedureiHpTarget1
Find the difference between the achieved power and the desired power for rejecting H1 using the improved trimmed or truncated weighted Hochberg procedure with allowance for different data maturitiesiHpTarget1m
Find the difference between the achieved power and the desired power for rejecting H2 using the improved trimmed or truncated weighted Hochberg procedureiHpTarget2
Find the difference between the achieved power and the desired power for rejecting H2 using the improved trimmed or truncated weighted Hochberg procedure with allowance for different data maturitiesiHpTarget2m
Calculate the x-coordinates of a function where zero crossings occurinterpolate_zero
Power for rejecting H1 using various types of the Hochberg ProcedureitwcHochPower
The two-step algorithm to calculate the best k value for the improved trimmed Hochberg method to ensure that the maximum type I error rate reaches alpha exactly when rho is arbitraryoptk
Calculate the rho value that reaches the maximum type I error rate in the improved trimmed Hochberg method when k value is givenoptrho
Compute the optimal sample size for the improved trimmed weighted Hochberg procedureoptsamplesize_iHp
Compute the optimal sample size for the improved trimmed weighted Hochberg procedure with allowance for different data maturitiesoptsamplesize_iHpm
Compute the optimal sample size for the weighted trimmed or truncated Hochberg procedureoptsamplesize_tHp
Compute the optimal sample size for the weighted Holm procedure with allowance for different data maturitiesoptsamplesize_wHolmpm
Compute the optimal sample size for the weighted Hochberg procedureoptsamplesize_wHp
Find the difference between the achieved power and the desired power for rejecting H1 using the weighted trimmed or truncated Hochberg proceduretHpTarget1
Find the difference between the achieved power and the desired power for rejecting H2 using the weighted trimmed or truncated Hochberg proceduretHpTarget2
Calculate the type I error rate of the weighted Simes testtypeIerror_Simes_mvtnorm
Calculate the type I error rate of the trimmed weighted Simes testtypeIerror_trimSimes_mvtnorm
Find the difference between the achieved power and the desired power for rejecting H1 using the weighted Holm procedurewHolmTarget1
Find the difference between the achieved power and the desired power for rejecting H1 using the weighted Holm procedure with allowance for different data maturitieswHolmTarget1m
Find the difference between the achieved power and the desired power for rejecting H2 using the weighted Holm procedurewHolmTarget2
Find the difference between the achieved power and the desired power for rejecting H2 using the weighted Holm procedure with allowance for different data maturitieswHolmTarget2m
Find the difference between the achieved power and the desired power for rejecting H1 using the weighted Hochberg procedurewHpTarget1
Find the difference between the achieved power and the desired power for rejecting H2 using the weighted Hochberg procedurewHpTarget2