% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xi_rolling_ccf.R
\name{run_rolling_xi_ccf}
\alias{run_rolling_xi_ccf}
\title{Rolling Directional Xi-CCF Analysis}
\usage{
run_rolling_xi_ccf(
  x,
  y,
  time_index = NULL,
  window_size,
  step_size = 1,
  max_lag,
  n_surr = 399,
  sig_level = 0.05,
  n_cores = NULL,
  save_dir = NULL
)
}
\arguments{
\item{x}{A numeric vector representing the first time series.}

\item{y}{A numeric vector representing the second time series.}

\item{time_index}{An optional vector representing timestamps.}

\item{window_size}{An integer specifying the size of the rolling window.}

\item{step_size}{An integer specifying the step size. Default is 1.}

\item{max_lag}{An integer specifying the maximum positive lag to compute.}

\item{n_surr}{An integer specifying the number of MIAAFT surrogate datasets. Default is 399.}

\item{sig_level}{A numeric value specifying the significance level (FWER). Default is 0.05.}

\item{n_cores}{An integer specifying the number of cores for parallel execution.}

\item{save_dir}{A character string specifying the directory path to save intermediate results.}
}
\value{
A \code{data.frame} containing the rolling window results.
}
\description{
Performs a rolling window analysis using Chatterjee's Xi cross-correlation to assess
the time-varying non-linear lead-lag relationship between two time series with FWER control.
}
