multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. R-project.org. R package version

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting er-roneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous infer-ence procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The frame-work described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth ∗This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363. Copyright ➞ 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim; available online http://www. biometrical-journal.com. 1 of the results. For the analyses we use the R add-on package multcomp, which pro-vides a convenient interface to the general approach adopted here. Key words: multiple tests, multiple comparisons, simultaneous confidence intervals, adjusted p-values, multivariate normal distribution, robust statistics.