% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{set_adaptive_quadrature_cpp}
\alias{set_adaptive_quadrature_cpp}
\title{Set up adaptive quadrature based on factor scores and standard errors}
\usage{
set_adaptive_quadrature_cpp(
  fm_ptr,
  factor_scores,
  factor_ses,
  factor_vars,
  threshold = 0.5,
  max_quad = 16L,
  verbose = TRUE
)
}
\arguments{
\item{fm_ptr}{External pointer to FactorModel object}

\item{factor_scores}{Matrix (n_obs x n_factors) of factor score estimates}

\item{factor_ses}{Matrix (n_obs x n_factors) of standard errors}

\item{factor_vars}{Vector (n_factors) of factor variances from previous stage}

\item{threshold}{Threshold for determining quadrature points (default 0.5, matching legacy)}

\item{max_quad}{Maximum quadrature points per factor (default 16)}

\item{verbose}{Whether to print summary of adaptive quadrature setup (default TRUE)}
}
\value{
No return value. Called for its side effect of enabling adaptive
quadrature on the FactorModel pointed to by \code{fm_ptr} and (when
\code{verbose = TRUE}) printing a summary of the per-observation
integration-point distribution.
}
\description{
Enables adaptive integration where the number of quadrature points varies
by observation based on the precision of factor score estimates. When factor
scores are well-determined (small SE), fewer integration points are used.
Importance sampling weights are computed automatically.
}
