Selbal-Style Forward Balance Feature Selection — codaFS_selbal

Wraps the official `selbal` implementation when it is available. In environments where `selbal` is not installed, or when `backend = "fallback"` is requested, the function uses a native forward balance-selection routine that greedily improves univariate logistic deviance on training data only.

Usage

codaFS_selbal_wrapper(
  X,
  y,
  n_features = NULL,
  positive = NULL,
  input_scale = c("log", "raw"),
  backend = c("auto", "official", "fallback"),
  max_terms = NULL,
  min_improvement = 1e-04,
  max_pair_features = 80L,
  prescreen_metric = c("clr_effect", "variance")
)

Arguments

X: Numeric matrix with samples in rows and features in columns.
y: Binary outcome vector.
n_features: Optional target panel size. Used as the default `max_terms`.
positive: Optional positive-class label for non-numeric outcomes.
input_scale: Either `"log"` (default) or `"raw"`.
backend: One of `"auto"`, `"official"`, or `"fallback"`.
max_terms: Maximum number of features in the selected balance. If `NULL`, defaults to `n_features`.
min_improvement: Minimum deviance improvement required to add a new feature during the fallback forward search.
max_pair_features: Maximum number of prescreened features considered by the forward search.
prescreen_metric: Either `"clr_effect"` or `"variance"`.

Value

A list with per-feature `scores`, `selected_features`, balance membership, and backend details.