R packages available on CRAN

NTSS, implementing nonparametric tests of various hypotheses for point processes and random fields, based on the random shift approach. Previous versions of the codes are also available below.

binspp, implementing Bayesian MCMC estimation for parameters of Thomas-type cluster point processes with various inhomogeneities. A paper describing the functionality of the package is available here.


Computer codes accompanying the individual papers

Nonparametric testing of the covariate significance for spatial point patterns under the presence of nuisance covariates

R codes for testing the covariate significance for spatial point patterns (source codes and examples).
Dvořák J. and Mrkvička T. (2022+). Nonparametric testing of the covariate significance for spatial point patterns under the presence of nuisance covariates. Submitted.

Nonparametric testing of the dependence structure among points-marks-covariates in spatial point patterns

R codes for testing independence between the points and a covariate (P-C test, here) and for testing independence between the marks and a covariate (PM-C test, here).
Dvořák J., Mrkvička T., Mateu J., and Gonzáles J.A. (2022+). Nonparametric testing of the dependence structure among points-marks-covariates in spatial point patterns. To appear in International Statistical Reviews.

Graphical tests of independence for general distributions

R codes for testing independence between two random variables (source codes and examples).
Dvořák J., and Mrkvička T. (2022). Graphical tests of independence for general distributions. Computational Statistics 37, 671-699.

Testing the first-order separability hypothesis for spatio-temporal point patterns

R codes for permutation-based tests of the separability hypothesis (example, source file with testing and plotting functions).
R codes for interactive visualization for assessing the separability hypothesis (example, source file with visualization functions, source file with estimation of space-time summaries).
R codes for stochastic reconstruction of space-time point patterns (example, source file with reconstruction procedure, source file with estimation of space-time summaries).
Ghorbani M., Vafaei N., Dvořák J., and Myllymäki M. (2021). Testing the first-order separability hypothesis for spatio-temporal point patterns. Computational Statistics and Data Analysis 161, 107245.

Revisiting the random shift approach for testing in spatial statistics

Source codes for testing independence of a pair of random fields (here) and for testing independence in a bivariate point process (using the cross K-function or the cross G-function).
Mrkvička T., Dvořák J., Gonzáles J.A., and Mateu J. (2021). Revisiting the random shift approach for testing in spatial statistics. Spatial Statistics 42, 100430.

Robust depth-based inference in elliptical models

A working R script with examples is available at the GEMS project website.
Nagy S., and Dvořák J. (2021). Robust depth-based inference in elliptical models. In Statistical Learning and Modeling in Data Analysis. CLADAG 2019. Studies in Classification, Data Analysis, and Knowledge Organization. (eds. S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco, M. Vichi) Springer, Cham, 129-137.

Clover plot: Versatile visualisation in nonparametric classification

A website with examples of the dynamic version of the clover plot, including several additional examples and a complete description of the R functions, is available at the GEMS project website, along with the relevant source codes.
Supplementary material to paper Dvořák J., Hudecová, Š., and Nagy S. (2020). Clover plot: Versatile visualisation in nonparametric classification. Statistical Analysis and Data Mining 13(6), 548-564.