significant differences can be defined. It is possible to analyze simultaneously
Depends: R (>= 2.10), prodlim, Rcpp, data.table
Imports: doParallel, foreach, methods, lava, parallel, stats, stats4,

person("Brice", "Ozenne", role = c("aut", "cre"), email = "brice.ozenne@orange.fr", comment = c(ORCID = "0000-0001-9694-2956")),
Suggests: knitr, pbapply, survival, testthat
Author: Brice Ozenne [aut, cre] (<https://orcid.org/0000-0001-9694-2956>),

Description: Implementation of the Generalized Pairwise Comparisons (GPC). GPC
compare two groups of observations (intervention vs. control
group) regarding several prioritized endpoints. The net benefit
and win ratio statistics can then be estimated and corresponding
confidence intervals and p-values can be estimated using
resampling methods or the asymptotic U-statistic theory. The software enables the use of thresholds of
minimal important difference as well as stratification. Censored observations
can be handled using Gehan's modification of the Wilcoxon statistic, inverse
probability of censoring weights, or, in the case of right
censoring, using the empirical survival curves in each group to
estimating the expected score of the censored pairs.
Suggests: pbapply, R.rsp, survival, testthat

Description: Implementation of the Generalized Pairwise Comparisons (GPC).
GPC compare two groups of observations (intervention vs. control group)
The net benefit and win ratio statistics can then be estimated
and corresponding confidence intervals and p-values can be
estimated using resampling methods or the asymptotic U-statistic
theory. The software enables the use of thresholds of minimal
importance difference, stratification, and corrections to deal
with right-censored endpoints or missing values.

person("Brice", "Ozenne", role = c("aut", "cre"), email = "brice.mh.ozenne@gmail.com", comment = c(ORCID = "0000-0001-9694-2956")),
person("Julien", "Peron", role = "ctb"),
person("Eva", "Cantagallo", role = "aut")
Description: Implementation of the Generalized Pairwise Comparisons (GPC)
as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations,
and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring.
regarding several prioritized endpoints to estimate the probability that a random observation drawn from
one group performs better than a random observation drawn from the other group.
The net benefit and win ratio statistics,
i.e. the difference and ratio between the probabilities relative to the intervention and control groups,
can then be estimated. Confidence intervals and p-values are obtained using permutations, a non-parametric bootstrap, or the asymptotic theory.
The software enables the use of thresholds of minimal importance difference,
stratification, non-prioritized endpoints (O'Brien test), and corrections to deal with missing values.
Imports: data.table, doParallel, foreach, methods, lava, parallel,
Suggests: cvAUC, pbapply, pROC, R.rsp, riskRegression, survival,