Changes in version 0.1.5                        

New functions

  - 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().

Improvements

  - 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.

Bug fixes

  - 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.

                 Changes in version 0.1.4 (2026-05-29)                  

Bug fixes

  - 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.

                 Changes in version 0.1.3 (2026-05-28)                  

New functions

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

Improvements

  - 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().

                 Changes in version 0.1.2 (2026-05-22)                  

Improvements

  - 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.

                 Changes in version 0.1.1 (2026-05-13)                  

New functions

  - 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.

Changes to existing functions

  - 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.

                 Changes in version 0.1.0 (2026-04-15)                  

  - Initial CRAN submission.