Compute log detection probabilities from a flat environmental data vector

C++ implementation of log_prob_detect() that accepts environmental data as a flat numeric vector with explicit dimension metadata. This signature is designed for block-by-block raster evaluation where each block is passed as a contiguous vector rather than a 3-D R array.

Usage

log_prob_detect_cpp(
  env_dat_vec,
  env_dat_dims,
  mu,
  sigltil,
  sigrtil,
  o_mat,
  ctil,
  pd,
  return_prob = FALSE,
  num_threads = 0L
)

Arguments

env_dat_vec: Numeric vector. Column-major flat representation of a 3-D array with logical dimensions c(n_loc, ts_length, p): variable k (1-indexed) occupies positions (k-1)*n_loc*ts_length + 1 to k*n_loc*ts_length, and within that block pixels (locations) vary fastest.
env_dat_dims: Integer vector of length 3: c(n_loc, ts_length, p).
mu: Numeric vector of length p. Optimal environmental values.
sigltil: Numeric vector of length p. Positive; Inf entries are allowed (treated as zero inverse-scale).
sigrtil: Numeric vector of length p. Positive; Inf entries are allowed.
o_mat: Numeric matrix, p x p orthogonal.
ctil: Scalar. Center of the detection-link function.
pd: Scalar in (0, 1]. Maximum probability of detection.
return_prob: Logical. If TRUE, return probabilities; if FALSE (default) return log-probabilities.
num_threads: Integer. Number of parallel threads. 0 (default) uses RcppParallel::defaultNumThreads().

Value

Numeric vector of length n_loc.

Details

Collapses the R call chain like_neg_ltsgr_cpp() -> like_ltsg() into a single xtensor-accelerated C++ function.