Added covercorr(), a unified front-end that dispatches to the appropriate
coverage correlation routine based on its input. It accepts a pair of
variables (x, y), a list of variables, or a matrix/data frame/data table
(each column treated as a variable), and chooses random or deterministic
reference points via the reference argument.
Added coverage_correlation_grid(), a variant of coverage_correlation()
that accepts user-supplied reference points (u, v) instead of generating
them randomly. When both inputs are one-dimensional and no points are
supplied, it defaults to the deterministic uniform grid {1/n, ..., 1},
making the rank transformation reproducible.
Added coverage_correlation_K(), which generalises the coefficient from a
pair of variables to K mutually compared variables supplied as a list.
Added coverage_correlation_K_grid(), the K-variable counterpart of
coverage_correlation_grid(), accepting an optional grid list of
reference points and defaulting to the uniform grid for one-dimensional
inputs.
All coverage correlation functions now return an object of class
"covercorr" with print() and summary() methods for readable output.
The fixed-grid pairwise p-value now uses the correct null centering for the deterministic-grid case, including the second-order term in the expansion of the null mean.
Explicitly supplied M (Monte Carlo sample size) is now coerced to an
integer and validated, so passing a plain numeric value no longer triggers a
low-level error.
NA handling now preserves matrix structure throughout (using
drop = FALSE), and rows are dropped consistently across all inputs and
reference grids.
K-variable
(K > 2) fixed-grid case. In that case coverage_correlation_K_grid()
returns pval = NA (with pval_available = FALSE) and emits an informative
message; the statistic itself is still computed. The K = 2 fixed-grid case
returns a valid p-value consistent with coverage_correlation_grid().coverage_correlation() for the coverage correlation
coefficient between two random variables or vectors, with exact and Monte
Carlo computation methods and optional visualisation.