GET: Global envelopes

The R package GET provides global envelopes which can be used for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot), 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 ANOVA, functional GLM, n-sample test of correspondence of distribution functions), and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction).

This is the development version

This repository holds a copy of the current development version of the contributed R package GET.

This development version is as or more recent than the official release of GET on the Comprehensive R Archive Network (CRAN) at

Where is the official release?

For the most recent official release of GET, see


Installing the official release

To install the official release of GET from CRAN, start R and type


Installing the development version

The easiest way to install the GET library from github is through the remotes package. Start R and type:


If you do not have the R library remotes installed, install it first by running


After installation, in order to start using GET, load it to R and see the main help page, which describes the functions of the library:



The branch for public use is called master. There are no other public branches at the moment. The branch ‘no_fastdepth’ of the library spptest was taken as the master branch of GET September 21, 2016. The spptest package is frozen to that version and no longer developed.


To cite GET in publications use

Myllymäki, M. and Mrkvička, T. (2019). GET: Global envelopes in R. arXiv:1911.06583 [stat.ME]

Myllymäki, M., Mrkvička, T., Grabarnik, P., Seijo, H. and Hahn, U. (2017). Global envelope tests for spatial processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79: 381-404. doi: 10.1111/rssb.12172 (You can find the preprint of the article here:

and a suitable selection of:

Mrkvička, T., Myllymäki, M. and Hahn, U. (2017). Multiple Monte Carlo testing, with applications in spatial point processes. Statistics and Computing 27 (5): 1239-1255.

Mrkvička, T., Myllymäki, M., Jilek, M. and Hahn, U. (2018). A one-way ANOVA test for functional data with graphical interpretation. arXiv:1612.03608 [stat.ME] (

Mrkvička, T., Myllymäki, M. and Narisetty, N. N. (2019). New methods for multiple testing in permutation inference for the general linear model. arXiv:1906.09004 [stat.ME]

Mrkvička, T., Roskovec, T. and Rost, M. (2019). A nonparametric graphical tests of significance in functional GLM. arXiv:1902.04926 [stat.ME]

Myllymäki, M., Grabarnik, P., Seijo, H., and Stoyan, D. (2015). Deviation test construction and power comparison for marked spatial point patterns. Spatial Statistics 11: 19-34. (You can find the preprint of the article here:

Mrkvička, T., Soubeyrand, S., Myllymäki, M., Grabarnik, P., and Hahn, U. (2016). Monte Carlo testing in spatial statistics, with applications to spatial residuals. Spatial Statistics 18, Part A: 40–53.