blockCV - Spatial and Environmental Blocking for Cross-Validation
Creates spatially or environmentally separated, or group-preserving, training and testing folds for k-fold, leave-group-out, and leave-one-out cross-validation. Provides spatial blocking, clustering, buffering, and nearest-neighbour distance-matching methods, together with tools to visualise folds, summarise fold sizes and class balance, and assess train–test separation and environmental novelty. Also estimates spatial autocorrelation ranges in point samples and continuous raster covariates to provide an initial distance scale for designing spatial folds. Methods are described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
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cross-validationspatialspatial-cross-validationspatial-modellingspecies-distribution-modellingcpp
11.53 score 123 stars 4 dependents 520 scripts 3.1k downloadsdisdat - Data for Comparing Species Distribution Modeling Methods
Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.
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6.13 score 2 stars 91 scripts 2.5k downloads
curves - Model-Agnostic Response Curves for Fitted Models
Create model-agnostic response-curve diagnostics for fitted prediction models. Supports profile curves, partial dependence, individual conditional expectation, and accumulated local effects; univariate curves, bivariate surfaces, ensemble summaries across multiple models, ALE-based interaction ranking, and optional raster-linked exploration with 'terra' and 'shiny'. Static displays are returned as 'ggplot2' plots. For more details on the methods see Molnar (2025) <https://christophm.github.io/interpretable-ml-book/>.
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partial-dependence-plotspdpresponse-curvesspatial-modellingspecies-distribution-modelling
4.88 score 3 stars 434 downloads