  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.
