loglik_niche_math_ip_weighted_integrated: Negative log-likelihood (inverse-probability-weighted normal,...

In xnicher: Estimates Ecological Niche Models Using Ellipses

Negative log-likelihood (inverse-probability-weighted normal, integrated C++)

Description

Low-level C++ bridge for the inverse-probability-weighted (IPW) normal niche model. Computes KDE weights and Mahalanobis distances entirely in C++ to minimise R overhead. This is the workhorse called by loglik_niche_math_ip_weighted.

Usage

loglik_niche_math_ip_weighted_integrated(
  theta,
  env_occ,
  env_m,
  eta = 1,
  neg = TRUE,
  den_idx = NULL,
  kde_idx = NULL,
  precomp_w_den = NULL
)

Arguments

theta	Numeric vector of unconstrained parameters (math scale): [\mu, \log\sigma, v] of length 2p + p(p-1)/2.
env_occ	Data frame or matrix (n \times p) of environmental values at presence points.
env_m	Data frame or matrix (n_m \times p) of background environmental values from the accessible area M.
eta	Numeric scalar, shape parameter for the LKJ C-vine prior on the correlation matrix (default 1 = uniform over correlations).
neg	Logical. If TRUE (default), returns the negative log-likelihood (suitable for minimisation).
den_idx	Integer vector of 1-based row indices for the denominator subsample. If NULL (default), uses all rows of env_m.
kde_idx	Integer vector of 1-based row indices for the KDE reference subsample. If NULL, uses all rows of env_m.
precomp_w_den	Numeric vector of precomputed KDE weights for the denominator points (must match den_idx in length). If provided, KDE for the denominator is not recomputed.

Details

The objective function is described in detail in loglik_niche_math_ip_weighted.

This function was formerly called loglik_niche_math_kde_bias_corrected_integrated.

Value

Scalar numeric: the (negative) log-likelihood.

See Also

[loglik_niche_math_ip_weighted()] for the user-facing wrapper with automatic subsampling.

Examples

theta <- start_theta(example_env_occ_2d)
ll <- loglik_niche_math_ip_weighted_integrated(
  theta   = theta,
  env_occ = example_env_occ_2d,
  env_m   = example_env_m_2d,
  eta     = 1,
  neg     = TRUE
)
print(ll)

xnicher documentation built on Sept. 15, 2026, 5:08 p.m.