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matrixCorr

matrixCorr is a lightweight, high-performance package for computing correlation matrices in R. Numerically robust estimates for:

• Kendall’s tau with automatic tau-a / tau-b selection (stable tie handling using merge-sort inversion counting + Fenwick trees),
• Spearman’s rho (rank + Pearson on ranks with average-tie ranking),
• Pearson’s r (single-pass, variance-checked).

Designed for large matrices and tie-heavy data, matrixCorr accepts matrices or data frames, returns symmetric correlation matrices with metadata, and includes convenient print() and plot() methods for quick inspection.

Features

• High-performance C++ backend using Rcpp
• Support for tied data via Kendall’s tau-b
• Easy-to-use kendall_tau() function for matrices and data frames
• ggplot2-based heatmap visualization
• Designed for large-scale, high-dimensional data

Installation

# Install from GitHub
# install.packages("devtools")
devtools::install_github("Prof-ThiagoOliveira/matrixCorr")

Example

Kendall’s Tau Example

library(matrixCorr)

# Simulated data
set.seed(42)
mat <- cbind(A = rnorm(100), B = rnorm(100), C = rnorm(100))

# Compute Kendall's tau correlation matrix
ktau <- kendall_tau(mat)

# Print matrix
print(ktau)

# Visualize with ggplot2
plot(ktau)

Spearman’s Rho Example

library(matrixCorr)

# Simulated data with some ties
set.seed(123)
mat <- cbind(
  A = sample(1:10, 100, replace = TRUE),
  B = sample(1:10, 100, replace = TRUE),
  C = rnorm(100)
)

# Compute Spearman's rho correlation matrix
spearman <- spearman_rho(mat)

# Print matrix
print(spearman)

# Visualize with ggplot2
plot(spearman)

Pearson Correlation Example

library(matrixCorr)

# Simulated continuous data
set.seed(999)
mat <- cbind(
  A = rnorm(100),
  B = 0.5 * rnorm(100) + 0.5,
  C = runif(100)
)

# Compute Pearson correlation matrix
pcorr <- pearson_corr(mat)

# Print matrix
print(pcorr)

# Visualize with ggplot2
plot(pcorr)

License

MIT Thiago de Paula Oliveira

See inst/LICENSE for the full MIT license text.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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