Collection of Correlation and Association Estimators
Abort with a standardised argument error
Abort for internal errors (should not happen)
Biweight mid-correlation (bicor)
Bland-Altman for repeated measurements
Bland-Altman statistics with confidence intervals
build_LDZ
ccc_lmm_reml_pairwise
Concordance Correlation via REML (Linear Mixed-Effects Model)
Repeated-Measures Lin's Concordance Correlation Coefficient (CCC)
Pairwise Lin's concordance correlation coefficient
Check AR(1) correlation parameter
Check a single logical flag
Check object class
Check matrix dimensions
Check probability in unit interval
Check that a data frame contains required columns
Check that two vectors have the same length
Check scalar character (non-empty)
Check strictly positive scalar integer
Check a non-negative scalar (>= 0)
Check scalar numeric (with optional bounds)
Check matrix symmetry
Check weights vector (non-negative, finite, correct length)
compute_ci_from_se
Pairwise Distance Correlation (dCor)
Align (optional named) weights to a subset of time levels
two-method helper
.vc_message
estimate_rho
Inform only when verbose
Pairwise (or Two-Vector) Kendall's Tau Rank Correlation
Match argument to allowed values
matrixCorr: Collection of Correlation and Association Estimators
num_or_na_vec
num_or_na
Partial correlation matrix (sample / ridge / OAS)
Pairwise Pearson correlation
S3 print for legacy class ccc_ci
Print method for matrixCorr CCC objects with CIs
Print method for matrixCorr CCC objects
run_cpp
Schafer-Strimmer shrinkage correlation
Pairwise Spearman's rank correlation
Summary Method for ccc_lmm_reml Objects
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) <doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005) <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.
Useful links