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blockCV 4.0-0

Added

• Added cv_knndm, a k-fold Nearest Neighbour Distance Matching method with "blocks", "hierarchical", and "kmeans" clustering options. It supports geographical and feature-space matching via space, and accepts prediction points via r, pred_points, or model_domain; issue #63.
• Added cv_distance, a diagnostic for comparing an existing blockCV fold design with the nearest-neighbour distance distribution expected in the prediction domain. It returns a cv_distance object carrying the per-fold test-to-nearest-train distance table in $distances, the Wasserstein-1 distances in $W, and the plot in $plot; a plot argument (default TRUE) controls only whether the plot is drawn.
• Added cv_summary, a one-call fold-quality diagnostic that gathers fold sizes and class prevalence, the cv_distance and cv_similarity diagnostics (when a raster or prediction domain is supplied), and a data.frame of warnings about degenerate folds (empty or single-class test folds, classes missing from training, tiny folds, severe class imbalance, and high leakage).
• Added cv_group, a leave-group-out cross-validation using an existing grouping column (e.g. site, plot, campaign, or individual) so records that share a group are never split across train and test. k = NULL gives one fold per group; a smaller k merges the groups into k folds, optionally balanced (balance = TRUE).
• Added balance to make fold balancing explicit in cv_spatial, cv_cluster, and cv_knndm. cv_cluster(balance = FALSE) keeps the previous single k-means behaviour; balance = TRUE uses the new k_multiplier argument to create candidate clusters for more even folds; issue #34.
• Added presence-background balancing support so cv_spatial, cv_cluster, and cv_knndm can base fold balancing or matching on presences, preventing background points from dominating the split.
• Added spatial_weight to cv_cluster for spatially-constrained environmental clustering. It adds a soft spatial compactness pressure by blending the (standardised) coordinates into the covariates before k-means, so the Euclidean distance becomes (1 - w) * d_env^2 + w * d_geo^2. spatial_weight = 0 (default) keeps the previous pure-environmental behaviour exactly, larger values give environmentally coherent folds that are also geographically separated, and 1 clusters on the coordinates alone.
• Added support for continuous or count column values in cv_spatial, cv_cluster, cv_buffer, cv_nndm, and cv_knndm. Numeric columns are binned with num_bins (default 4) for fold balancing and records reports; num_bins = NULL restores per-value handling.
• Added combine_folds and fold_colors to cv_plot for single-map fold plots in cv_spatial, cv_cluster, cv_group, and cv_knndm.
• Added bg_alpha to cv_plot to control the opacity of background points in presence-background fold plots; it is capped at points_alpha (with a warning) so the background is never drawn more prominent than the presences, and the "background shown faded" caption appears only when the background is actually more transparent.
• Added species_pb.csv, a presence-background example dataset used in the cv_buffer() and cv_cluster() examples.
• Added a second tutorial covering cv_summary, cv_similarity, cv_distance, and block-size selection with cv_spatial_autocor and cv_block_size.
• Added a fourth tutorial showing how to use blockCV folds with caret::trainControl() via index and indexOut; issue #48.

Changed

• The minimum R version is now 3.6.0.
• Renamed the num_plot argument of cv_similarity to num_plots, for consistency with cv_plot (a breaking change for existing code that set num_plot).
• Removed the sp dependency (a retiring package) from cv_spatial_autocor. Longlat inputs now pass an explicit great-circle cutoff to automap::autofitVariogram instead of being converted to a sp object, so automap (>= 1.1-20) is now required.
• Reworked the fold-balancing search behind cv_spatial (random selection) and now cv_cluster(balance = TRUE). The objective is now scored on the test folds only and normalised per class (a Pearson chi-square against an equal split), and it minimises empty test cells for classes with at least k records before minimising imbalance. This replaces the previous rule (raise the smallest cell, then lower the standard deviation of all train and test cells at once), which could keep a split that leaves a class missing from a test fold and let an abundant class dominate the score. Because a different candidate assignment now wins, folds for a given seed may differ from earlier versions.
• cv_similarity now computes presence-background similarity from presences only, matching cv_distance, and adds seed for reproducible random baselines. L1/L2 distance-based scores now handle single-layer rasters, single-point folds, missing covariates, and constant predictors more robustly, and the MESS score now guards constant (zero-range) predictors. cv_similarity also validates that the rows of x line up with the fold indices in cv, matching cv_distance.
• Reports, plots, and progress bars now run by default only in interactive sessions for cv_spatial, cv_buffer, cv_cluster, and cv_nndm; cv_similarity progress bars now do the same. Report flags now suppress printing only; fold record tables are still returned. Issue #57.
• The print methods for the fold objects (cv_spatial, cv_buffer, cv_cluster, cv_group, cv_knndm, cv_nndm) and for cv_spatial_autocor now show an informative summary (method description, key settings, and the per-fold train/test record table) instead of only echoing the class name, matching the diagnostic print methods of cv_distance, cv_similarity, and cv_summary. Each returns the object invisibly. The cv_cluster print distinguishes spatial, environmental (feature-space), and spatially-constrained environmental clustering, and the returned object now carries the spatial_weight used. The cv_spatial object now carries the block_shape ("hexagon", "square", or "user-defined") and the selection used, both shown in its print. The cv_distance and cv_similarity prints now note when the object is presence-background, making it clear the distance/similarity/novelty statistics are computed on presence points only (background samples excluded).
• Functions that need a suggested package now stop with a clear installation message instead of an interactive install prompt, so they behave consistently in scripts and non-interactive sessions.
• cv_cluster now rejects categorical (factor) layers in r with a clear error instead of silently clustering their integer codes, since environmental clustering uses k-means (Euclidean distance) and supports only numeric covariates. It also warns when spatial (coordinate-based) clustering runs on geographic (lon/lat) coordinates, since k-means uses Euclidean distance; consider projecting x. It now also validates k against the number of available sample points (or sampled raster cells) before calling stats::kmeans, stopping with a clearer message instead of the cryptic "more cluster centers than distinct data points" error.
• The cv_* functions now raise a clear error when x cannot be coerced to an sf object, instead of silently continuing with the invalid input and failing later with an unrelated message.
• cv_nndm no longer requires r when pred_points or model_domain are supplied, now falls back to Euclidean distances with a warning when x has no CRS, and errors when x and r have mismatched coordinate reference systems; issue #58.
• cv_nndm now returns exclusion, a table with one row per fold giving the fold number, the row index of its test point in x (test_id), and the exclusion_distance matched to that point, so it is clear which record is held out at what distance. Unlike cv_buffer, whose exclusion distance is the constant size, NNDM matches a distinct radius to each test point; the radius is not capped by size, as points whose nearest neighbour already lies beyond size are left unthinned and keep their larger natural nearest-neighbour distance.
• cv_similarity now returns a cv_similarity object (a list with the per-fold extrapolation summary in $extrapolation, the overall novelty rate in $overall, and the plot in $plot) instead of a ggplot with the summary attached as an attribute. It gains a plot argument (default TRUE) that controls only whether the plot is drawn; the plot object is always built and returned. The overall novelty rate is shown in the title and the novel region is shaded, and a new type = "map" plots the sample points in space, colouring each test point by its similarity to show where extrapolation occurs.
• Improved the fold-object plot() methods, now including cv_spatial, so they accept the original sample data and report missing data more clearly; issues #59 and #60.
• cv_spatial avoids duplicate block-to-point intersection work when subsetting and assigning blocks; issue #40.

Removed

• Removed the legacy v2.x functions: buffering, envBlock, foldExplorer, rangeExplorer, spatialAutoRange, and spatialBlock; use the cv_* equivalents in new code.

Fixed

• Fixed a train/test leakage bug in cv_nndm(presence_bg = TRUE) where the test fold used the presence's position instead of its row index, holding out the wrong point and leaving it in both train and test.
• Fixed plot.cv_spatial_autocor() so single-layer autocorrelation objects plot correctly and multi-layer plots include the stored map panel.
• cv_plot and cv_similarity now stop with a clear message when none of the requested num_plots folds exist (e.g. num_plots = 99 for a 5-fold object), instead of silently reducing the selection to nothing and failing later with a confusing "undefined columns selected" error; out-of-range values are still dropped when at least one requested fold is valid.

blockCV 3.2.0

• Added two new distance-based similarity scores, L1 and L2, to the cv_similarity function
• Fixed a warning in cv_similarity for colour aesthetics with ggplot
• Fixed the summary method and plotting for cv_spatial_autocor

blockCV 3.1.7

• Temporarily added sp package dependency to avoid CRAN error as required by automap package [#55].

blockCV 3.1.6

• Resolved unclear error messages; issue #52 by A. Márcia Barbosa
• Resolved ggplot testing failure; issue #54 by Teun van den Brand

blockCV 3.1.5

• Resolved background pattern artefacts in raster plotting; issue #50 by Camila Neder.

blockCV 3.1.4

• Just the biomod2 example is updated in vignettes; and the link in help file

blockCV 3.1.3

• the biomod2 and gstat packages are added to the Suggests section
• Some minor edits in messages

blockCV 3.1.2

• The iteration in cv_spatial and spatialBlock is increased to 100 to make the result matches with v2.1.4
• Removed the requirement of C++11
• Some warnings are added for the miss use of the column argument

blockCV 3.1.1

• some internal fix.
• the extend parameter is now added to spatialBlock and the function now uses cv_spatial internally.
• the user_blocks in cv_spatial is restricted to random and predefined and systematic selection.
• no raster package dependency

blockCV 3.1.0

• the result of the cv_spatial function for square blocks now matches the one of version 2 function spatialBlock (i.e. fold assignment starts from top-right corner; this is not the case for hexagon blocks)
• square spatial blocks can be expanded to ensure no points fall outside the border of the blocks. This can be controlled by extend parameter now.

blockCV 3.0.3

• fixing a bug in counting records in the reporting of cv_spatial

blockCV 3.0.2

• fixing fold numbering of cv_spatial to reproducibility of earlier versions

blockCV 3.0.1

• Massive performance improvement in the C++ code of cv_nndm function for large datasets

blockCV 3.0

• Dependency to rgdal and rgeos are removed, and overall less dependency
• Function names have been changed, with all functions now starting with cv_
• The old functions (v2.x) still work to allow appropriate time for adapting the new code
• The CV blocking functions are now: cv_spatial, cv_cluster, cv_buffer, and cv_nndm
• Spatial blocks now support hexagonal (default), rectangular, and user-defined blocks
• A fast C++ implementation of Nearest Neighbour Distance Matching (NNDM) algorithm (Milà et al. 2022) is now added
• The NNDM algorithm can handle species presence-background data and other types of data
• The cv_cluster function generates blocks based on kmeans clustering. It now works on both environmental rasters and the spatial coordinates of sample points
• The cv_spatial_autocor function now calculates the spatial autocorrelation range for both the response (i.e. the binary or continuous data) and a set of continuous raster covariates
• The new cv_plot function allows for visualization of folds from all blocking strategies using ggplot facets
• The terra package is now used for all raster processing and supports both stars and raster objects, as well as files on disk.
• The new cv_similarity provides measures on possible extrapolation to testing folds

blockCV 2.1.4 (2021-06-17)

• fixed CRAN error for ggplot guide
• added rgdal as a suggest
• changed the crs of raster data in the package to avoid datum warnings

blockCV 2.1.3

• fix the warning for spatialBlock function on geographic coordinate system

blockCV 2.1.2

• predefined folds from user-defined blocks are noe accepted
• add seed argument to spatialBlock to have consistent results where needed

blockCV 2.1.0

• snowfall package for parallel processing is replaces by future.apply package; #7
• future.apply, shiny, shinydashboard, geosphere and ggplot2 packages moved to SUGGESTION packages. These are not install by default and the user is asked if needed. #7
• RStoolbox is no longer used for clustering
• an argument is added to envBlock function for sampling from raster layers
• no dependency on sp package any more
• doParallel = FALSE by default in spatialAutoRange function

blockCV 2.0.1

• print() and cat() are removed from the functions and verbose argument is added instead

blockCV 2.0.0

• most of the underlying functions are migrated to sf functions;
• the parallel processing changed from foreach to snowfall;
• the species argument in spatialBlock function accepts multi-class responses to find evenly distributed records in train and test folds;

blockCV 1.1.0

• change spatialAutoRange function to accepts rasters with low number of pixels; #2
• the maskBySpecies = FALSE in spatialBlock function is no longer supported;
• the numLimit argument in spatialBlock function is only accepts numeric values, and 0 means searching for evenly distributed folds;

blockCV 1.0.1

• add speciesData to spatialAutoRange function;