R package GET: Global Envelopes. 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 (2020) <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>.

References in zbMATH (referenced in 15 articles )

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  1. Berry, Eric; Chen, Yen-Chi; Cisewski-Kehe, Jessi; Fasy, Brittany Terese: Functional summaries of persistence diagrams (2020)
  2. Biscio, Christophe A. N.; Chenavier, Nicolas; Hirsch, Christian; Svane, Anne Marie: Testing goodness of fit for point processes via topological data analysis (2020)
  3. Cuevas, Francisco; Allard, Denis; Porcu, Emilio: Fast and exact simulation of Gaussian random fields defined on the sphere cross time (2020)
  4. Dai, Wenlin; Mrkvička, Tomáš; Sun, Ying; Genton, Marc G.: Functional outlier detection and taxonomy by sequential transformations (2020)
  5. Mrkvička, Tomáš; Myllymäki, Mari; Jílek, Milan; Hahn, Ute: A one-way ANOVA test for functional data with graphical interpretation. (2020)
  6. Xu, Meng; Reiss, Philip T.: Distribution-free pointwise adjusted (p)-values for functional hypotheses (2020)
  7. Andersson, C.; Rajala, T.; Särkkä, A.: A Bayesian hierarchical point process model for epidermal nerve fiber patterns (2019)
  8. Dai, Wenlin; Genton, Marc G.: Directional outlyingness for multivariate functional data (2019)
  9. Heinrich, Lothar: Asymptotic goodness-of-fit tests for point processes based on scaled empirical (K)-functions (2018)
  10. Baddeley, Adrian; Hardegen, Andrew; Lawrence, Thomas; Milne, Robin K.; Nair, Gopalan; Rakshit, Suman: On two-stage Monte Carlo tests of composite hypotheses (2017)
  11. Mrkvička, Tomáš; Myllymäki, Mari; Hahn, Ute: Multiple Monte Carlo testing, with applications in spatial point processes (2017)
  12. Myllymäki, Mari; Mrkvička, Tomáš; Grabarnik, Pavel; Seijo, Henri; Hahn, Ute: Global envelope tests for spatial processes (2017)
  13. Stoica, Radu S.; Philippe, Anne; Gregori, Pablo; Mateu, Jorge: ABC shadow algorithm: a tool for statistical analysis of spatial patterns (2017)
  14. Cronie, O.; van Lieshout, M. N. M.: Summary statistics for inhomogeneous marked point processes (2016)
  15. Møller, Jesper; Ghorbani, Mohammad; Rubak, Ege: Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data (2016)