https://cran.r-project.org/package=GET
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).
The github 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 https://cran.r-project.org/package=GET
For the most recent official release of GET, see
https://cran.r-project.org/package=GET
To install the official release of GET from CRAN, start
R and type
install.packages('GET')The easiest way to install the GET library from github
is through the remotes package. Start R and
type:
require(remotes)
install_github('myllym/GET')If you do not have the R library remotes installed,
install it first by running
install.packages("remotes")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:
require(GET)
help('GET-package')If you want to have also vignettes working, you should also install packages from the ‘suggests’ field, have MiKTeX on your computer, and install the library with
install_github('myllym/GET', build_vignettes = TRUE)The package contains four vignettes. The GET vignette describes the
package in general. It is available by starting R and
typing
library("GET")
vignette("GET")This vignette corresponds to Myllymäki and Mrkvička (2023).
The package provides also a vignette for global envelopes for point
pattern analyses, which is available by starting R and
typing
library("GET")
vignette("pointpatterns")The third vignette describes and provides code for the examples of Mrkvička and Myllymäki (2023) using the false discovery rate (FDR) envelopes,
library("GET")
vignette("FDRenvelopes")Finally, the fourth vignette, available by
library("GET")
vignette("HotSpots")shows how the methodology proposed by Mrkvička et al. (2023b) for
detecting hotspots on a linear network can be performed using
GET.
All vignettes are also available at the package webpage https://cran.r-project.org/package=GET
Currently two branches are provided in the development version. The
main branch of GET is called master.
The other branches are called FDR and
quantileregression. The FDR branch includes
also the experimental FDR envelopes tested in Mrkvička and Myllymäki
(2023). The main branch includes the FDR envelopes which were found to
have good performance in Mrkvička and Myllymäki (2023).
We note that the quantileregression branch, which
included the implementation of the global quantile regression proposed
in Mrkvička et al. (2023a), was recently merger to the
master.
To cite GET in publications use
Myllymäki, M. and Mrkvička, T. (2024). GET: Global envelopes in R. Journal of Statistical Software 111(3), 1-40. doi: 10.18637/jss.v111.i03 https://doi.org/10.18637/jss.v111.i03
and a suitable selection of:
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 http://dx.doi.org/10.1111/rssb.12172 (You can find the preprint version of the article here: http://arxiv.org/abs/1307.0239v4)
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. https://doi.org/10.1016/j.spasta.2014.11.004 (You can find the preprint version of the article here: http://arxiv.org/abs/1306.1028)
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. https://doi.org/10.1016/j.spasta.2016.04.005
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. https://doi.org/10.1007/s11222-016-9683-9
Mrkvička, T., Myllymäki, M., Jilek, M. and Hahn, U. (2020). A one-way ANOVA test for functional data with graphical interpretation. Kybernetika 56 (3), 432-458. http://doi.org/10.14736/kyb-2020-3-0432
Myllymäki, M., Kuronen, M. and Mrkvička, T. (2020). Testing global and local dependence of point patterns on covariates in parametric models. Spatial Statistics 42, 100436. https://doi.org/10.1016/j.spasta.2020.100436
Mrkvička, T., Roskovec, T. and Rost, M. (2021). A nonparametric graphical tests of significance in functional GLM. Methodology and Computing in Applied Probability 23, 593-612. https://doi.org/10.1007/s11009-019-09756-y
Dai, W., Athanasiadis, S. and Mrkvička, T. (2022). A new functional clustering method with combined dissimilarity sources and graphical interpretation. Intech open. https://doi.org/10.5772/intechopen.100124
Dvořák, J. and Mrkvička, T. (2022). Graphical tests of independence for general distributions. Computational Statistics 37, 671–699. https://doi.org/10.1007/s00180-021-01134-y
Mrkvička, T., Myllymäki, M., Kuronen, M. and Narisetty, N. N. (2022). New methods for multiple testing in permutation inference for the general linear model. Statistics in Medicine 41(2), 276-297. https://doi.org/10.1002/sim.9236
Mrkvička and Myllymäki (2023). False discovery rate envelopes. Statistics and Computing 33, 109. https://doi.org/10.1007/s11222-023-10275-7
Mrkvička, T., Konstantinou, K., Kuronen, M. and Myllymäki, M. (2023a). Global quantile regression. arXiv:2309.04746 [stat.ME] https://doi.org/10.48550/arXiv.2309.04746
Mrkvička T., Kraft S., Blažek V., Myllymäki M. (2023b). Hotspot detection on a linear network in the presence of covariates: a case study on road crash data. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4627591
Konstantinou, K., Mrkvička, T. and Myllymäki, M. (2024). Graphical n-sample tests of correspondence of distributions. arXiv:2403.01838 [stat.ME] https://doi.org/10.48550/arXiv.2403.01838