High-Performance Open-Source Archive
h3sdm_filter_range() filters spatial predictions
outside the univariate range of training data. Adds a
range_filter column (1 = within range, 0 = outside range)
to the prediction object. Complements h3sdm_aoa() and
h3sdm_filter_outliers(): while AOA detects combinations
without analogues in the training data and Mahalanobis distance
identifies multivariate outliers, this function detects extrapolation at
the margins of individual variables that multivariate methods may not
capture.
h3sdm_pres_from_sf() assigns pre-downloaded species
occurrence records (an sf object, typically from
h3sdm_get_records()) to H3 hexagons, returning only
hexagons with at least one presence record. This enables a clean
two-stage workflow where records are downloaded once and reused:
h3sdm_get_records() → h3sdm_pres_from_sf() →
h3sdm_filter_outliers() → h3sdm_pa() with
balanced pseudo-absences
(n_pseudoabs = nrow(pres_clean)).
cr_outline – Costa Rica full outline (continental
landmass + Isla del Coco and all minor oceanic islands), derived from
GADM 4.1.h3sdm_filter_outliers() removes environmental
outliers from presence records prior to model training using Mahalanobis
distance (D2) in environmental space. Only presences
(presence == "1") are evaluated; pseudo-absences are always
retained unchanged. The outlier threshold is derived from the
chi-squared distribution (qchisq(threshold, df = k),
default threshold = 0.975). Returns a list with the cleaned
PA dataset, a data frame of removed records with their D2 values, the
count of removed records, and the threshold value used. Complements
h3sdm_aoa(): while the AOA evaluates prediction reliability
after training, this function improves input data quality before
training.
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().
cr_outline_c dataset regenerated from GADM 4.1 with
a fully reproducible script in data-raw/cr_outline.R.
Source attribution updated to GADM 4.1. Geometry is now consistent with
the cr_outline_c dataset in paisaje.
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.
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.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().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.
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.
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