Imports: ggplot2, graphics, grDevices, gridExtra, gstat, methods,
parallel, plyr, sp, spatstat, stats, utils
Suggests: fda.usc, testthat, mvtnorm, fda
Description: Implementation of global envelopes with intrinsic graphical interpretation which can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2019) <arXiv:1911.06583>, Myllymäki et al. (2017) <doi: 10.1111/rssb.12172>, Mrkvička et al. (2017) <doi: 10.1007/s11222-016-9683-9>, Mrkvička et al. (2016) <doi: 10.1016/j.spasta.2016.04.005>, Mrkvička et al. (2018) <arXiv:1612.03608>, Mrkvička et al. (2019) <arXiv:1906.09004>, Mrkvička et al. (2019) <arXiv:1902.04926>.

Description: Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% 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 alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2019) <arXiv:1911.06583>, Myllymäki et al. (2017) <doi: 10.1111/rssb.12172>, Mrkvička et al. (2017) <doi: 10.1007/s11222-016-9683-9>, Mrkvička et al. (2016) <doi: 10.1016/j.spasta.2016.04.005>, Mrkvička et al. (2018) <arXiv:1612.03608>, Mrkvička et al. (2019) <arXiv:1906.09004>, Mrkvička et al. (2019) <arXiv:1902.04926>.