% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/changepoint-scr-recovery.R
\name{detect_gazepoint_doubly_stochastic_changepoints}
\alias{detect_gazepoint_doubly_stochastic_changepoints}
\title{Detect stochastic change points in noisy biometric signals}
\usage{
detect_gazepoint_doubly_stochastic_changepoints(
  dat,
  signal_col,
  time_col = "CNT",
  group_cols = NULL,
  window_seconds = 10,
  step_seconds = 2,
  threshold_mad_multiplier = 6,
  min_distance_s = 5
)
}
\arguments{
\item{dat}{A data frame.}

\item{signal_col}{Numeric signal column.}

\item{time_col}{Numeric time column.}

\item{group_cols}{Optional grouping columns.}

\item{window_seconds}{Window length in seconds.}

\item{step_seconds}{Step size in seconds.}

\item{threshold_mad_multiplier}{Robust threshold multiplier.}

\item{min_distance_s}{Minimum distance between detected change points.}
}
\value{
A list with \code{overview}, \code{score_table}, \code{changepoints}, and \code{settings}.
}
\description{
Detects abrupt changes in noisy biological time series using a dependency-light
stochastic rolling-window approximation. The score combines adjacent-window
changes in mean and variance with a robust adaptive threshold.
}
\details{
This is not a full reproduction of any specific doubly stochastic model. It
is a transparent approximation for QC and exploratory segmentation.
}
