https://elkronos.r-universe.devRecent package updates in elkronosR-universehttps://github.com/elkronos.pnghttps://elkronos.r-universe.devWed, 07 Oct 2026 22:07:41 GMTjchase.msu@gmail.com (Justin Chase)Constructs analysis regions from the distribution of the data itself, as an alternative to aggregating onto administrative boundaries that were drawn for unrelated purposes. Seeds and builds Voronoi, Delaunay, hexagonal and square tessellations with reproducible identifiers, selects a cell count from the spatial structure of the observations, assigns features to cells, and aggregates to cell level with optional design-effect corrections so that standard errors account for within-cell autocorrelation. Also manages coordinate reference systems. Fits geographically weighted regression (via 'GWmodel'; Lu et al. (2014) <doi:10.1080/10095020.2014.917453>), Bayesian spatial Gaussian process regression (via 'brms', using the Hilbert space approximation of Riutort-Mayol et al. (2023) <doi:10.1007/s11222-022-10167-2>) and random forests (via 'ranger', with the permutation importance of Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>), each behind one S3 class with consistent predict, fitted, residuals and plot methods. Provides spatial cross-validation with random, block, buffered, leave-location-out and nearest-neighbour distance-matched folds (Mila et al. (2022) <doi:10.1111/2041-210X.13851>), forward variable selection, model comparison, prediction onto a regular surface, and the area of applicability of Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650> to flag where a fitted model extrapolates beyond its training data.https://github.com/r-universe/elkronos/actions/runs/37696551037Wed, 07 Oct 2026 22:07:41 GMTspatialkit2.0.0.9000successhttps://elkronos.r-universe.devhttps://github.com/elkronos/gis_modeling_toolkitresolution.Rmdresolution.htmlChoosing a resolution2026-09-17 01:48:082026-09-27 04:55:24diagnostics.Rmddiagnostics.htmlDiagnostics2026-09-17 01:48:082026-09-27 01:38:29getting-started.Rmdgetting-started.htmlGetting started2026-09-17 01:48:082026-09-27 01:38:29spatial-cross-validation.Rmdspatial-cross-validation.htmlSpatial cross-validation2026-09-17 01:48:082026-09-27 01:38:29spatialkit_nc_demo.Rmdspatialkit_nc_demo.htmlspatialkit: Tessellations, Spatial Cross-Validation and Models2026-07-30 06:33:182026-09-27 01:38:29reporting.Rmdreporting.htmlWhat to save, and what to report2026-09-19 00:05:092026-09-27 01:38:29jchase.msu@gmail.com (Justin Chase)Filter, wrapper, and embedded feature-selection methods behind a consistent set of functions that share one calling convention and one return type: correlation and chi-squared filters, information gain, LASSO and elastic net, Bayesian model comparison, Boruta, recursive feature elimination, random forest importance, multivariate adaptive regression splines, support vector machine recursive feature elimination, stepwise selection, and principal component / singular value decomposition helpers. The implemented methods follow Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, Friedman (1991) <doi:10.1214/aos/1176347963>, Breiman (2001) <doi:10.1023/A:1010933404324>, Guyon, Weston, Barnhill and Vapnik (2002) <doi:10.1023/A:1012487302797>, Kursa and Rudnicki (2010) <doi:10.18637/jss.v036.i11>, and Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. Heavy modeling engines are optional and only required by the functions that use them.https://github.com/r-universe/elkronos/actions/runs/36306025491Sun, 27 Sep 2026 03:56:43 GMTfeatR0.1.0successhttps://elkronos.r-universe.devhttps://github.com/elkronos/featrfeatR.RmdfeatR.htmlGetting started with featR2026-08-24 05:36:262026-09-27 03:15:30