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Version 2.0.0

  • Added a boosted condition estimator to benchmark(), reference_use(), variable_importance(), and hcas_inspection(). The default is now boost = k2, with k1 = 70 and k2 = 10: the highest-probability retained site's kernel weight is multiplied by the boost factor before computing condition, condition attribution, or variable-importance weights. Set boost = NULL or boost = NA to use the previous unboosted LDC/ordinary-kernel behaviour.
  • Renamed the public k_pred and k_obs arguments to k1 and k2, respectively, to identify them as the first- and second-stage reference filters rather than imply that each filter is tied exclusively to an environmental or observed-RS definition.
  • Added an experimental expanded temporal reference mode to benchmark(). It is off by default; supply temporal_sigma to enable it. temporal_sigma controls the bandwidth of the Gaussian temporal weighting used to select and weight the most relevant reference year for each site. Reference samples can be supplied as a named list of yearly matrices. This expands the temporal representation of reference sites rather than applying a post-hoc correction to the target condition score.
  • Added selectable retained-reference distance kernels. kernel = "Gaussian" remains the default and uses exp(-(distance / lambda)^2); kernel = "Cauchy" uses the standard Cauchy shape 1 / (1 + (distance / lambda)^2). Lower-case gaussian and cauchy are also accepted. The same option is used by benchmark(), reference_use(), hcas_inspection(), and variable_importance().
  • Added raster target support to hcas_inspection(). Raster inputs use x/y target coordinates, can extract reference-sample values from xy-only samples, and support Leaflet map clicks that snap the clicked location to the containing raster cell centre.
  • Added reference_use() to assess how reference sites are used during benchmarking. A single run returns each site's predicted selection count among k1, density retention count among k2, and condition attribution. Its attribution uses the selected distance kernel and boost setting. Expanded temporal references are not yet supported by reference_use().
  • Added variable_importance() to attribute the observed target-to-reference departure to each RS variable. The output argument selects per-variable layers of "importance" (default; signal-to-noise ratio: the weighted mean absolute departure of the target from its references, standardised by the weighted spread among the references themselves), "signal" (the raw weighted departure contribution, which sums per cell to the total observed departure), or "share" (the per-cell departure partition, where a cell's variables sum to one — a direct percent attribution). aggregate_importance() summarises the per-cell scores across the landscape into a median ranking and a mean relative-share attribution. Its retained-reference weighting uses the same selected distance kernel and boost setting as benchmark(). Expanded temporal references are not yet supported.

Version 1.1.0

  • Consolidated package colour helpers into a single palettes() function and removed hcas_color() and ref_density_color().
  • Changed drop_features so excluded RS variables are removed from the active feature set before C++ distance calculations, instead of being zeroed in place.

Version 1.0.0

  • Renamed the reference density function to ref_density(), renamed the radial counting function (from proximity()) to radial_count(), and changed the reference density object class to reference_density.
  • The order of input data has completely changed and both ref_density() and benchmark() functions require data with x, y, predicted, and observed remote sensing variable order.
  • Complete re-write of the main C++ functions (for ref_density(), benchmark(), and radial_count()) using vectorised operations, including changing feature space distance calculations from double to float32, resulting in significant speed improvement (~10x) with no loss of accuracy.
  • The Eigen C++ library is adapted as the main matrix engine.
  • The dependency on KDtrees is dropped while keeping or even improving the speed.
  • The reference density now performs only one-way pairwise distance calculations. As a result, the raw reference density values are exactly halved compared to before. This has no impact on the normalised reference density or the final output.
  • The "corner value" in the reference density (previously calculated as the count of values) is no longer computed, since it is simply equal to the number of samples.
  • The calibrate() function no longer performs interpolation. The output is now fully fitted using a monotonic spline.
  • A fast spatial distance calculation is implemented (no difference for projected CRS with 0.01 meters accuracy).
  • The geographic distance for lat/long is now corrected for the latitude of the source cell, not just the radius transformation to degrees.
  • A new function (tiling()) is added for making raster tiles using raster or matrix data.
  • The NaN pixels are now directly handled within C++ code.
  • Expanded the test suite with additional C++-focused coverage, including edge cases and thread-consistency checks for core workflows.

Version 0.2.0

  • Added drop_features parameter to fully exclude specific remote sensing variables from both the ref_density and benchmark functions.
  • Added a condition to ensure that k_obs is less than or equal to k_pred.
  • A new reference density normalisation method has been implemented in R using the legacy = FALSE argument to mitigate edge effects.
  • The zero-zero point (reference self-count) in the raw reference density is now excluded prior to normalisation.
  • The ref_density function arguments have changed to data and samples.

Version 0.1.6

  • Added a scale_factor parameter to allow user-defined correction of geographic CRS distance calculations, enhancing flexibility in handling distance conversions (previously set to a default value only).
  • Enhanced C++ code for improved efficiency in point class creation.

Version 0.1.5

  • Replaced the natural spline in the calibrate function with a monotonically increasing spline function.

Version 0.1.4

  • Deprecated the linear (piece-wise) calibration method, superseded with the spline method.
  • The arguments of calibrate are updated with x_values and y_values.
  • Fixed the benchmarking self_exclude floating point error.

Version 0.1.3

  • Added the proximity function to count the number of samples within a specified radius.
  • Removed the requirement for add_xy in the benchmark() function.
  • Removed filename and wopt parameters from all functions and replaced them with additional arguments ... for more flexibility.
  • Internal terra functions have been improved by eliminating the need for explicit creation of x and y coordinates.

Version 0.1.2

  • Check for availability of the terra package is added when input is a raster
  • improved documentation and imports
  • a bug fix in benchmarking

Version 0.1.1

  • Added the geographic distance penalty for selecting the nearest predicted neighbours (xy_penalty and xy_stats parameters)
  • Added an option for excluding self-assessment for benchmark sample sites (default).

Version 0.1.0

  • Translated all legacy HCAS codes to R and Rcpp