Imports: mvtnorm, multcomp, mratios, graphics, stats
Packaged: 2017-07-31 08:55:18 UTC; Administrator

Description: Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).
Packaged: 2019-08-26 13:46:48 UTC; Administrator

Description: Provides a framework to generating random variates from
arbitrary multivariate copulae, while concentrating on
(bivariate) extreme value copulae.  Particularly useful if
the multivariate copulae are not available in closed
Imports: grDevices, graphics, quadprog, stats

Title: Simulates Correlated Multinomial Responses
Description: This package simulates correlated multinomial responses
utilizing threshold approaches and assuming a cumulative link
model or a baseline category logit model for the univariate

Description: Simulates correlated multinomial responses by utilizing
threshold approaches under a marginal cumulative link model, a
marginal continuation ratio model or a marginal baseline

Description: Simulates correlated multinomial responses by utilizing threshold approaches under a cumulative link model, a continuation ratio model or a baseline category logit marginal model specification.

Description: Simulates correlated multinomial responses by utilizing threshold approaches under a cumulative link model, a continuation ratio model or a baseline-category logit marginal model specification.

Description: Simulates correlated multinomial responses by utilizing threshold
approaches under a marginal model specification.

Description: Simulates correlated multinomial responses conditional on a marginal model specification.
Authors@R: c(person("Anestis", "Touloumis", role = c("aut", "cre"), email = "A.Touloumis@brighton.ac.uk"))