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matrixCorr (version 0.8.3)

matrixCorr-internal: matrixCorr: Collection of Correlation and Association Estimators

Description

Compute correlation and other association matrices from small to high-dimensional datasets with relative simple functions and sensible defaults. Includes options for shrinkage and robustness to improve results in noisy or high-dimensional settings (p >= n), plus convenient print/plot methods for inspection. Implemented with optimised C++ backends using BLAS/OpenMP and memory-aware symmetric updates. Works with base matrices and data frames, returning standard R objects via a consistent S3 interface. Useful across genomics, agriculture, and machine-learning workflows. Supports Pearson, Spearman, Kendall, distance correlation, partial correlation, and robust biweight mid-correlation; Bland–Altman analyses and Lin's concordance correlation coefficient (including repeated-measures extensions). Methods based on Ledoit and Wolf (2004) tools:::Rd_expr_doi("10.1016/S0047-259X(03)00096-4"); Schäfer and Strimmer (2005) tools:::Rd_expr_doi("10.2202/1544-6115.1175"); Lin (1989) tools:::Rd_expr_doi("10.2307/2532051").

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)

Value

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

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.

Author

Maintainer: Thiago de Paula Oliveira thiago.paula.oliveira@gmail.com (ORCID)

Thiago de Paula Oliveira

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.

See Also

Useful links:

pearson_corr(), spearman_rho(), kendall_tau()