email = "Torsten.Hothorn@R-project.org", comment = c(ORCID = "0000-0001-8301-0471")),
Depends: stats, graphics, mvtnorm (>= 1.0-10), survival (>= 2.39-4),
Suggests: lme4 (>= 0.999375-16), nlme, robustbase, coin, MASS, foreign,
xtable, lmtest, coxme (>= 2.2-1), SimComp, ISwR, tram (>=

Depends: stats, graphics, mvtnorm (>= 1.0-3), survival (>= 2.39-4),

Title: Visualizations of Paired Comparisons
Author: Spencer Graves and Hans-Peter Piepho with
Description: Convert a logical vector or a vector of
p-values or a correlation, difference, or distance
matrix into a display identifying the pairs for
which the differences were not significantly
different.  Designed for use in conjunction with
the output of functions like TukeyHSD, dist{stats},
Packaged: Sun Aug  6 18:10:27 2006; spencerg

Author: Spencer Graves and Hans-Peter Piepho with help from Sundar
Description: Convert a logical vector or a vector of p-values or a
correlation, difference, or distance matrix into a display
identifying the pairs for which the differences were not
significantly different.  Designed for use in conjunction with
the output of functions like TukeyHSD, dist{stats}, simint,

Author: Spencer Graves, Hans-Peter Piepho and Luciano Selzer with help

Author: Spencer Graves, Hans-Peter Piepho and Luciano Selzer with

Description: The Multiregression Dynamic Models (MDM) are a multivariate
graphical model for a multidimensional time series that allows the estimation of

Description: Multiregression Dynamic Models for directed dynamic functional brain network analysis.

Title: Optimal Exact Tests for Multiple Binary Endpoints
Authors@R: person("Robin", "Ristl", email = "robin.ristl@meduniwien.ac.at", role = c("aut", "cre"))
Description: Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated
that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple