% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/online-design-optimization.R
\name{run_gazepoint_online_design_optimization}
\alias{run_gazepoint_online_design_optimization}
\title{Run blockwise online design optimization decision support}
\usage{
run_gazepoint_online_design_optimization(
  candidate_table,
  condition_col = "condition",
  utility_col = "expected_utility",
  block_col = NULL,
  cost_col = NULL,
  previous_assignments = NULL,
  exploration_weight = 0.1,
  balance_weight = 0.1,
  maximise = TRUE
)
}
\arguments{
\item{candidate_table}{A data frame containing candidate conditions.}

\item{condition_col}{Candidate condition column.}

\item{utility_col}{Expected utility/model-discrimination column.}

\item{block_col}{Optional block column.}

\item{cost_col}{Optional cost or burden column subtracted from utility.}

\item{previous_assignments}{Optional previous condition assignments.}

\item{exploration_weight}{Weight for favouring under-sampled conditions.}

\item{balance_weight}{Weight for penalising over-sampled conditions.}

\item{maximise}{Logical. If \code{TRUE}, select highest score.}
}
\value{
A list with \code{overview}, \code{ranked_candidates}, \code{recommendation},
\code{assignment_summary}, and \code{settings}.
}
\description{
Provides a safe, dependency-light decision-support/simulation helper for
online design optimization. The function recommends the next condition by
combining expected model-discrimination utility with optional exploration and
balancing penalties.
}
\details{
This function does not control stimulus presentation software and should not
be used as autonomous real-time experiment control without separate ethical,
preregistration, and software-integration review.
}
