matrixCorr-internal: Validation and normalisation for correlation

In matrixCorr: Collection of Correlation and Association Estimators

Validation and normalisation for correlation

Description

Validates and normalises input for correlation computations. Accepts either a numeric matrix or a data frame, filters numeric columns, checks dimensions and (optionally) missing values, and returns a numeric (double) matrix with preserved column names.

Usage

validate_corr_input(data, check_na = TRUE)

Arguments

data	A matrix or data frame. Non-numeric columns are dropped (data.frame path). For matrix input, storage mode must be integer or double.
check_na	Logical (default TRUE). If TRUE, validate and reject inputs containing NA/NaN/Inf. Set to FALSE when an upstream routine (e.g., pairwise-complete kernels) will handle missingness per pair.

Details

Rules enforced:

• Input must be a matrix or data.frame.

• Only numeric (integer or double) columns are retained (data.frame path).

• At least two numeric columns are required.

• All columns must have the same length and \ge 2 observations.

• Missing values are not allowed when check_na = TRUE.

• Returns a double matrix; integer input is converted once.

Value

A numeric matrix (type double) with column names preserved.

Author(s)

Thiago de Paula Oliveira

See Also

pearson_corr(), spearman_rho(), kendall_tau()

matrixCorr documentation built on April 18, 2026, 5:06 p.m.