Changelog • h3sdm

h3sdm 0.1.5

h3sdm_pres() assigns species occurrence records to H3 hexagons and returns only hexagons with at least one presence record. This is the first step of a two-stage workflow where pseudo-absences are generated after environmental variables have been extracted.
h3sdm_pa() has been redesigned to generate pseudo-absences stratified in environmental space using k-means clustering. Pseudo-absences now cover the full range of environmental conditions available in the AOI, reducing environmental bias introduced by spatially clustered occurrence records. The function now receives presence hexagons from h3sdm_pres() and the full hexagonal grid with extracted variables from h3sdm_predictors().

h3sdm_pa() and h3sdm_pa_from_records() now accept a buffer_k argument (default 1). Hexagons within buffer_k H3 rings of any presence hexagon are excluded from the pseudo-absence candidate pool, preventing pseudo-absences from being placed in areas likely occupied but not yet recorded. Set to 0 to disable.
h3sdm_pa_from_records() now accepts an optional predictors_sf argument. When provided, pseudo-absences are selected by stratified sampling in environmental space using k-means clustering. If NULL (default), the previous random geographic sampling behaviour is preserved.

h3sdm_aoa() now extracts predictor variable names from the model recipe instead of the model formula, fixing an error with GLM and other engines where parsnip stores a generic formula internally.
h3sdm_aoa() now uses na.rm = TRUE when computing the inside/outside AOA summary, avoiding NA in the progress message when hexagons have missing values.

CRAN release: 2026-05-29

h3sdm_pa() now transforms presence records to the CRS of the H3 grid before joining, fixing an error when the grid is in a projected CRS.

CRAN release: 2026-05-28

h3sdm_aoa() estimates the Dissimilarity Index (DI) and the Area of Applicability (AOA) for spatial prediction models, based on Meyer & Pebesma (2021).

h3sdm_get_grid() now preserves the CRS of the input sf_object. Previously, the function always returned the grid in WGS84 (EPSG:4326) regardless of the input CRS. Now, if the AOI is in a projected CRS, the output grid will be reprojected to match it. The internal H3 computation still uses WGS84 as required by the H3 system.
h3sdm_predict() internal comments translated to English and @seealso updated to include h3sdm_aoa().

CRAN release: 2026-05-22

h3sdm_fit_model() now automatically detects model mode (classification or regression), enabling count-based models (Poisson, Negative Binomial) with appropriate metrics (RMSE, R², MAE) without requiring manual configuration.
h3sdm_fit_model() and h3sdm_predict() now automatically detect model mode (classification or regression), enabling count-based models (Poisson, Negative Binomial) without manual configuration. Full backward compatibility maintained.
h3sdm_get_records() now supports "biodatacr" as an optional provider, querying occurrence records from BiodataCR (Costa Rica) via the rbiodatacr package. h3sdm_pa() inherits this support automatically through its providers argument. rbiodatacr is listed as a suggested dependency.

CRAN release: 2026-05-13

h3sdm_pa_from_records(): generates a presence/pseudo-absence dataset from user-provided records. Accepts a data.frame or sf object with coordinates in WGS84 (EPSG:4326). Supports optional filtering by a geospatialKosher column to remove records with questionable spatial quality.
h3sdm_count_from_records(): generates a hexagonal grid with count-based response variables (species richness, total detections, or individual abundance) from user-provided records. Accepts a data.frame or sf object. Supports optional filtering by presence column, confidence threshold, and date range.

h3sdm_recipe(): added response_col parameter (default "presence") to support count-based response variables. Use response_col = "count" when working with data generated by h3sdm_count_from_records().
h3sdm_recipe_gam(): added response_col parameter (default "presence") with the same behavior as h3sdm_recipe(). Also added documentation examples for both presence/absence and count-based models.
h3sdm_workflow_gam(): updated documentation to clarify the use of set_mode("classification") for presence/absence models and set_mode("regression") with family = poisson() for count-based models.
h3sdm_workflow(): updated documentation to clarify model mode selection for presence/absence and count-based models.
h3sdm_workflows(): updated documentation to clarify model mode selection for presence/absence and count-based models.

CRAN release: 2026-04-15