Suggests: Rgraphviz, RUnit, knitr, highlight, RJSONIO, RCurl, Rcpp

Title: Cohen's D_p Computation with Confidence Intervals
Description: Computing Cohen's d_p in any experimental designs (between-subject, within-subject, and single-group design). Cousineau (2022) <https://github.com/dcousin3/CohensdpLibrary>; Cohen (1969, ISBN: 0-8058-0283-5).
Authors@R: c(person(given = "Denis", family = "Cousineau",
Packaged: 2022-08-16 12:15:05 UTC; DenisCousineau
Author: Denis Cousineau [aut, cre] (Developer)

person(given = "Douglas", family = "Fitts",
Packaged: 2022-08-22 14:01:41 UTC; DenisCousineau
Author: Denis Cousineau [aut, cre] (Developer),

Title: Simulation of Cohort Platform Trials for Combination Treatments
Maintainer: Elias Laurin Meyer <elias.meyer@meduniwien.ac.at>
Description: A collection of functions dedicated to simulating staggered entry platform
trials whereby the treatment under investigation is a combination of two
active compounds. In order to obtain approval for this combination therapy,
superiority of the combination over the two active compounds and superiority
of the two active compounds over placebo need to be demonstrated.
A more detailed description of the design can be found in
Imports: dplyr, purrr, ggplot2, plotly, tidyr, parallel, doParallel,
foreach, openxlsx, forcats, epitools, zoo

Authors@R: c(person("Elias Laurin", "Meyer", email = "elias.meyer@meduniwien.ac.at", role = c("aut","cre")),
person("Peter", "Mesenbrink", role = "ctb"),
person("Cornelia", "Dunger-Baldauf", role = "ctb"),
person("Ekkehard", "Glimm", role = "ctb"),
person("Franz", "Koenig", role = "ctb"))
Meyer et al. <DOI:10.1002/pst.2194> and a manual in Meyer et al. <arXiv:2202.02182>.

Package: CoinMinD
Type: Package
Title: Simultaneous Confidence Interval for Multinomial Proportion
Version: 1.0
Date: 2013-04-22
Author: Dr M.Subbiah
Maintainer: Sumathi <sumathimr@yahoo.co.in>
Description: Methods for obtaining simultaneous confidence interval for
multinomial proportion have been proposed by many authors and
the present study include a variety of widely applicable
procedures. Seven classical methods (Wilson, Quesenberry and
Hurst, Goodman, Wald with and without continuity correction,
Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet
models are included in the package. The advantage of MCMC pack
has been exploited to derive the Dirichlet posterior directly
and this also helps in handling the Dirichlet prior parameters.
This package is prepared to have equal and unequal values for
the Dirichlet prior distribution that will provide better scope
for data analysis and associated sensitivity analysis.
License: GPL-2
Depends: MCMCpack
Packaged: 2013-04-23 15:07:31 UTC; Sumathi
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-04-23 21:19:18