Suggests: pbapply, R.rsp, riskRegression, survival, testthat

one group performs better than a random observation drawn from the other group (Mann-Whitney parameter).
can then also be estimated. Confidence intervals and p-values are obtained using permutations, a non-parametric bootstrap, or the asymptotic theory.
stratification, non-prioritized endpoints (O'Brien test), and can handle right-censoring and competing-risks.

Imports: data.table, doParallel, foreach, ggplot2, methods, lava,

can then also be estimated. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>),
stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.
parallel, prodlim, riskRegression, stats, stats4, utils
Suggests: cvAUC, mvtnorm, pbapply, pROC, R.rsp, survival, testthat
Collate: '0-onLoad.R' '1-setGeneric.R' 'BuyseMultComp.R' 'BuyseTTEM.R'

Suggests: cvAUC, mvtnorm, pbapply, pROC, R.rsp, riskRegression,

Title: B-Value and Empirical Equivalence Bound
Description: Calculates B-value and empirical equivalence bound. B-value is defined as the maximum magnitude of a confidence interval; and the empirical equivalence bound is the minimum B-value at a certain level. A new two-stage procedure for hypothesis testing is proposed, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using the introduced empirical equivalence bound. See Zhao et al. (2019) "B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing" <arXiv:1912.13084> for details.

Title: Bandwidth Selectors for Local Linear Quantile Regression
person("Mercedes", "Conde-Amboage", role = c("aut", "cre"),
email = "mercedes.amboage@usc.es", comment = c(ORCID = "0000-0003-0306-8142")),
person("Cesar", "Sanchez-Sellero", role = "aut",
email = "cesar.sanchez@usc.es",comment = c(ORCID = "0000-0003-0046-0819"))
Depends: R (>= 2.6), quantreg, KernSmooth, nleqslv
Description: Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.
Author: Mercedes Conde-Amboage [aut, cre]