To cite package ‘spatialkit’ in publications use:

  Chase J (2026). _spatialkit: Spatial Tessellation, Modeling, and
  Cross-Validation Toolkit_. R package version 2.0.0.9000,
  <https://github.com/elkronos/gis_modeling_toolkit>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {spatialkit: Spatial Tessellation, Modeling, and Cross-Validation Toolkit},
    author = {Justin Chase},
    year = {2026},
    note = {R package version 2.0.0.9000},
    url = {https://github.com/elkronos/gis_modeling_toolkit},
  }

spatialkit implements published methods. If you use one of them, please
cite the method as well as the package:

area_of_applicability(): Meyer, H. and Pebesma, E. (2021). Predicting
into unknown space? Estimating the area of applicability of spatial
prediction models. Methods in Ecology and Evolution 12(9), 1620-1633.
doi:10.1111/2041-210X.13650

make_folds(method = "nndm"): Mila, C., Mateu, J., Pebesma, E. and
Meyer, H. (2022). Nearest neighbour distance matching leave-one-out
cross-validation for map validation. Methods in Ecology and Evolution
13(6), 1304-1316. doi:10.1111/2041-210X.13851

fit_bayesian_spatial_model() GP basis: Riutort-Mayol, G., Burkner,
P.-C., Andersen, M. R., Solin, A. and Vehtari, A. (2023). Practical
Hilbert space approximate Bayesian Gaussian processes for probabilistic
programming. Statistics and Computing 33, 17.
doi:10.1007/s11222-022-10167-2

fit_rf_model() permutation importance: Strobl, C., Boulesteix, A.-L.,
Zeileis, A. and Hothorn, T. (2007). Bias in random forest variable
importance measures: illustrations, sources and a solution. BMC
Bioinformatics 8, 25. doi:10.1186/1471-2105-8-25

fit_rf_model(include_coords =) and cv_rf(): Meyer, H., Reudenbach, C.,
Wollauer, S. and Nauss, T. (2019). Importance of spatial predictor
variable selection in machine learning applications - moving from data
reproduction to spatial prediction. Ecological Modelling 411, 108815.
doi:10.1016/j.ecolmodel.2019.108815

fit_gwr_model() and gwr_model_selection(): Lu, B., Harris, P.,
Charlton, M. and Brunsdon, C. (2014). The GWmodel R package: further
topics for exploring spatial heterogeneity using geographically
weighted models.  Geo-spatial Information Science 17(2), 85-101.
doi:10.1080/10095020.2014.917453
