% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{cor_adjust_pearson}
\alias{cor_adjust_pearson}
\title{Convert Pearson correlations to Gaussian copula correlations}
\usage{
cor_adjust_pearson(X, types)
}
\arguments{
\item{X}{Correlation matrix (Pearson)}

\item{types}{Character vector of distribution types}
}
\value{
Adjusted correlation matrix for Gaussian copula
}
\description{
For continuous-continuous pairs, Pearson rho equals the Gaussian copula
parameter only under normality of both margins. For non-normal continuous
covariates, this no-adjustment assumption introduces approximation error.
For binary and mixed pairs:
\itemize{
\item Binary-binary: rho_copula = sin(pi * rho_P / 2)
\item Continuous-binary: rho_copula = sqrt(pi/2) * rho_P
}
}
\keyword{internal}
