Train MaxEnt directly from a terra SpatRaster — maxent_train

High-level convenience wrapper that builds a streaming FeaturedSpace from rast, maps occurrence locations to finite-stream sample indices, and trains with maxent_fit.

Usage

maxent_train_terra(
  rast,
  occurrences,
  lon_col = "longitude",
  lat_col = "latitude",
  feature_types = c("linear", "quadratic", "product", "threshold", "hinge"),
  n_thresholds = 10L,
  n_hinges = 10L,
  max_iter = 500L,
  convergence = 1e-05,
  beta_multiplier = 1,
  min_deviation = 0.001
)

Arguments

rast: A terra::SpatRaster.
occurrences: Either a two-column matrix/data.frame (x,y) or a vector of 1-based raster cell indices.
lon_col: Longitude column name when occurrences is a data.frame.
lat_col: Latitude column name when occurrences is a data.frame.
feature_types: Feature type set passed to feature generation.
n_thresholds: Number of threshold knots.
n_hinges: Number of hinge knots.
max_iter: Maximum training iterations.
convergence: Convergence threshold.
beta_multiplier: Regularization multiplier.
min_deviation: Minimum deviation floor.

Value

A named list with training results plus model and sample_indices.