latentcor: An R Package for estimating latent correlations from mixed data types

TL;DR

The paper introduces `latentcor`, an R package that accurately estimates correlations among mixed data types using semi-parametric latent Gaussian copula models, suitable for high-throughput data analysis.

Contribution

It provides a comprehensive R package implementing latent Gaussian copula models for mixed data correlation estimation, improving accuracy and efficiency over traditional methods.

Findings

Efficient multi-linear interpolation reduces memory usage.

Accurate correlation estimation across diverse mixed data types.

Applicable to high-throughput data analysis workflows.

Abstract

We present `latentcor`, an R package for correlation estimation from data with mixed variable types. Mixed variables types, including continuous, binary, ordinal, zero-inflated, or truncated data are routinely collected in many areas of science. Accurate estimation of correlations among such variables is often the first critical step in statistical analysis workflows. Pearson correlation as the default choice is not well suited for mixed data types as the underlying normality assumption is violated. The concept of semi-parametric latent Gaussian copula models, on the other hand, provides a unifying way to estimate correlations between mixed data types. The R package `latentcor` comprises a comprehensive list of these models, enabling the estimation of correlations between any of continuous/binary/ternary/zero-inflated (truncated) variable types. The underlying implementation takes advantage of a fast multi-linear interpolation scheme with an efficient choice of interpolation grid points, thus giving the package a small memory footprint without compromising estimation accuracy. This makes latent correlation estimation readily available for modern high-throughput data analysis.