This work describes the R package GET that implements global envelopes for a general set of d-dimensional vectors T in various applications. A 100(1 - α)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to α. The term 'global' means that this probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be employed for central regions of functional or multivariate data, for graphical Monte Carlo and permutation tests where the test statistic is multivariate or functional, and for global confidence and prediction bands. Intrinsic graphical interpretation property is introduced for global envelopes. The global envelopes included in the GET package have this property, which particularly helps to interpret test results, by providing a graphical interpretation that shows the reasons of rejection of the tested hypothesis. Examples of different uses of global envelopes and their implementation in the GET package are presented, including global envelopes for single and several one- or two-dimensional functions, Monte Carlo goodness-of-fit tests for simple and composite hypotheses, comparison of distributions, functional analysis of variance, functional linear model, and confidence bands in polynomial regression.

In Europe, Lyme borreliosis (LB) and tick-borne encephalitis (TBE) are the two vector-borne diseases with the largest impact on human health. Based on data on the density of host-seeking Ixodes ricinus ticks and pathogen prevalence and using a variety of environmental data, we have created an acarological risk model for a region where both diseases are endemic (Czech Republic—South Bohemia and Germany—Lower Bavaria, Upper Palatinate). The data on tick density were acquired by flagging 50 sampling sites three times in a single season. Prevalence of the causative agents of LB and TBE was determined. Data on environmental variables (e.g., altitude, vegetation cover, NDVI, land surface temperature) were obtained from various sources and processed using geographical information systems. Generalized linear models were used to estimate tick density, probability of tick infection, and density of infected ticks for the whole area. A significantly higher incidence of human TBE cases was recorded in South Bohemia compared to Bavarian regions, which correlated with a lower tick density in Bavaria. However, the differences in pathogen prevalence rates were not significant. The model outputs were made available to the public in the form of risk maps, indicating the distribution of tick-borne disease risk in space.