#' Simulated null distribution for one-factor CFA fit and loadings under PCM unidimensionality
#'
#' Generates a parametric-bootstrap null distribution against which observed
#' one-factor categorical-CFA results can be compared. The simulation draws
#' \code{iterations} datasets from the fitted PCM (or RM, for dichotomous
#' data) using the observed item parameters and a resampled person
#' distribution; each simulated dataset is fitted with
#' \code{lavaan::cfa(..., ordered = TRUE, estimator = "WLSMV")} and both the
#' three fit indices (CFI, RMSEA, SRMR) and the per-item standardized factor
#' loadings are recorded. Because the simulated data satisfy the PCM
#' unidimensional assumption by construction, the resulting distributions are
#' the "expected" reference for what a correctly fitting unidimensional model
#' produces at this sample size and item structure.
#'
#' This function only generates the simulated reference. To obtain the
#' observed-vs-expected tables, pass its result to \code{\link{RMdimCFA}}; for
#' the figures, pass it to \code{\link{RMdimCFAPlot}}.
#'
#' @param data A data.frame or matrix of item responses (non-negative
#'   integers, 0-based). One column per item, one row per person.
#' @param iterations Integer. Number of parametric-bootstrap iterations.
#'   Default `250`.
#' @param percentile Numeric in (50, 100). The strictness of the cutoffs.
#'   Default `99`. Fit indices use a one-sided cutoff in the unfavourable
#'   direction (CFI from below; RMSEA and SRMR from above); standardized
#'   loadings use a two-sided central interval covering `percentile`% of the
#'   simulated distribution (an item is flagged when its observed loading
#'   falls in the outer `100 - percentile`%, split across the two tails).
#' @param output Character. Only `"list"` (the default) is supported and the
#'   function always returns the simulation object. `"kable"` is retained only
#'   to raise an informative error: tables now come from
#'   \code{\link{RMdimCFA}}.
#' @param parallel Logical. If `TRUE` (default), uses parallel processing
#'   via `mirai`. Falls back to sequential if `mirai` is not installed or
#'   `n_cores` cannot be resolved.
#' @param n_cores Integer or `NULL`. Number of parallel workers. When
#'   `NULL`, `getOption("mc.cores")` is consulted; if neither is set,
#'   sequential is used.
#' @param verbose Logical. Show a progress bar (default `FALSE`).
#' @param seed Integer or `NULL`. Master seed for reproducibility.
#' @param estimator Character. The lavaan estimator passed to
#'   `lavaan::cfa()`. Default `"WLSMV"`. Other limited-information
#'   estimators that produce robust/scaled fit indices (e.g.,
#'   `"DWLS"`, `"ULSMV"`) are also accepted; full-information ML is
#'   rejected (incompatible with `ordered = TRUE`).
#'
#' @return A list (the simulation object), with components:
#' \describe{
#'   \item{`simulated`}{data.frame with one row per successful iteration
#'     and columns `iteration`, `cfi`, `rmsea`, `srmr`.}
#'   \item{`simulated_loadings`}{data.frame with one row per successful
#'     iteration: an `iteration` column followed by one column per item
#'     holding the simulated standardized loading.}
#'   \item{`percentile`}{Numeric: the strictness setting used.}
#'   \item{`cutoffs`}{Named numeric vector (`cfi`, `rmsea`, `srmr`) of
#'     one-sided fit-index cutoffs at the chosen percentile.}
#'   \item{`loading_cutoffs`}{data.frame `Item`, `low`, `high` — the
#'     two-sided expected loading interval per item.}
#'   \item{`actual_iterations`}{Number of successful MC iterations.}
#'   \item{`sample_n`}{Number of complete cases used.}
#'   \item{`sample_n_total`}{Number of respondents in the raw input data,
#'     before the complete-case filter.}
#'   \item{`sample_has_na`}{Logical. Whether the raw input data contained
#'     any missing values.}
#'   \item{`n_items`}{Number of items.}
#'   \item{`item_names`}{Character vector of item names.}
#'   \item{`is_polytomous`}{Logical: was a PCM (vs RM) fitted?}
#'   \item{`estimator`}{The lavaan estimator used.}
#' }
#'
#' @details
#' \strong{Generative model.} The data-generating process for each
#' simulated dataset is the PCM (or RM) fitted to the observed data,
#' with persons drawn from the empirical theta distribution
#' (resampled with replacement). This means the simulated data perfectly
#' satisfy the PCM unidimensional assumption.
#'
#' \strong{Estimation model.} The CFA on each simulated dataset uses a
#' single-factor model with all items as ordinal indicators
#' (`F1 =~ I1 + I2 + ...`), fitted with `WLSMV` by default. Reported
#' CFI / RMSEA are the Satorra-Bentler-scaled variants (`cfi.scaled`,
#' `rmsea.scaled`) for consistency across iterations; SRMR is reported
#' unchanged. Standardized loadings are the `est.std` of the `=~` paths
#' from `lavaan::standardizedSolution()`.
#'
#' \strong{Why a null distribution.} A perfectly PCM-unidimensional
#' dataset will typically not yield CFA fit indices at their ideal
#' values (CFI = 1, RMSEA = 0), nor identical loadings across items: PCM
#' uses a logistic threshold structure while WLSMV uses a probit link via
#' the polychoric correlation matrix, and finite samples add sampling
#' variability. The simulated distributions capture both, giving a more
#' honest reference than rule-of-thumb cutoffs derived under continuous-data
#' ML.
#'
#' \strong{Iteration failures.} Some simulated datasets cause WLSMV to
#' fail (non-positive-definite polychoric matrix, boundary thresholds,
#' empty categories). Failed iterations are dropped; `actual_iterations`
#' reflects the number that succeeded.
#'
#' @references
#' Yuan, K.-H., & Bentler, P. M. (2000). Three likelihood-based methods
#' for mean and covariance structure analysis with nonnormal missing
#' data. \emph{Sociological Methodology, 30}(1), 165-200.
#' \doi{10.1111/0081-1750.00078}
#'
#' Rosseel, Y. (2012). lavaan: An R Package for Structural Equation
#' Modeling. \emph{Journal of Statistical Software, 48}(2), 1-36.
#' \doi{10.18637/jss.v048.i02}
#'
#' @seealso \code{\link{RMdimCFA}}, \code{\link{RMdimCFAPlot}},
#'   \code{\link{RMdimResidualPCA}}, \code{\link{RMdimMartinLof}}
#'
#' @examples
#' \donttest{
#' if (requireNamespace("lavaan", quietly = TRUE) &&
#'     requireNamespace("eRm", quietly = TRUE)) {
#'   data("raschdat1", package = "eRm")
#'
#'   # Few iterations for a fast example; use 250+ in real analyses
#'   sim <- RMdimCFACutoff(raschdat1[, 1:8], iterations = 50,
#'                         parallel = FALSE, seed = 1)
#'
#'   # Observed-vs-expected tables
#'   RMdimCFA(raschdat1[, 1:8], cutoff = sim)
#'
#'   if (requireNamespace("ggplot2", quietly = TRUE)) {
#'     plots <- RMdimCFAPlot(sim, data = raschdat1[, 1:8])
#'     plots$loadings
#'     plots$fit
#'   }
#' }
#' }
#'
#' @export
RMdimCFACutoff <- function(
  data,
  iterations = 250L,
  percentile = 99,
  output = c("list", "kable"),
  parallel = TRUE,
  n_cores = NULL,
  verbose = FALSE,
  seed = NULL,
  estimator = "WLSMV"
) {
  output <- match.arg(output)
  if (output == "kable") {
    stop(
      "RMdimCFACutoff() now returns the simulation object only (a list).\n",
      "Use RMdimCFA(data, cutoff = <this object>) for the fit and loadings ",
      "tables, and RMdimCFAPlot(<this object>, data) for the figures.",
      call. = FALSE
    )
  }

  if (!requireNamespace("lavaan", quietly = TRUE)) {
    stop(
      "Package 'lavaan' is required for RMdimCFACutoff(). ",
      "Install with: install.packages(\"lavaan\")",
      call. = FALSE
    )
  }

  if (
    !is.numeric(percentile) ||
      length(percentile) != 1L ||
      !is.finite(percentile) ||
      percentile <= 50 ||
      percentile >= 100
  ) {
    stop(
      "`percentile` must be a single numeric in (50, 100). ",
      "Common choices: 95, 99, 99.5.",
      call. = FALSE
    )
  }

  estimator <- toupper(estimator[1L])
  if (estimator %in% c("ML", "MLR", "MLM", "MLF")) {
    stop(
      "`estimator = \"",
      estimator,
      "\"` is not appropriate for ",
      "ordinal-CFA. Use a limited-information estimator such as ",
      "\"WLSMV\", \"DWLS\", or \"ULSMV\".",
      call. = FALSE
    )
  }

  validate_response_data(data)

  # rgl workaround
  old_rgl <- getOption("rgl.useNULL")
  options(rgl.useNULL = TRUE)
  on.exit(options(rgl.useNULL = old_rgl), add = TRUE)

  # Only complete cases. Record the raw total and whether anything was
  # dropped so callers (e.g. RMdimCFAPlot) can report the sample in the
  # standard `n = X of Y respondents` form.
  n_total <- nrow(as.data.frame(data))
  has_na <- anyNA(data)
  data <- stats::na.omit(as.data.frame(data))
  if (nrow(data) == 0L) {
    stop("No complete cases in `data`.", call. = FALSE)
  }
  if (ncol(data) < 3L) {
    stop(
      "RMdimCFACutoff() requires at least 3 items for a one-factor CFA.",
      call. = FALSE
    )
  }

  # Parallel setup -- mirrors RMdimResidualPCACutoff()
  use_parallel <- parallel && requireNamespace("mirai", quietly = TRUE)
  if (parallel && !use_parallel) {
    message(
      "Install 'mirai' for parallel processing: install.packages(\"mirai\")"
    )
    message("Running sequentially...")
  }
  if (use_parallel) {
    if (is.null(n_cores)) {
      n_cores <- getOption("mc.cores")
    }
    if (is.null(n_cores)) {
      warning(
        "For parallel processing, specify n_cores or set options(mc.cores = N).\n",
        "Falling back to sequential.",
        call. = FALSE
      )
      use_parallel <- FALSE
    } else {
      n_cores <- min(n_cores, iterations)
    }
  }

  if (!is.null(seed)) {
    set.seed(seed)
  }
  sim_seeds <- sample.int(.Machine$integer.max, iterations)

  data_mat <- as.matrix(data)
  sample_n <- nrow(data_mat)
  is_polytomous <- max(data_mat, na.rm = TRUE) > 1L
  item_names_vec <- colnames(data_mat)
  if (is.null(item_names_vec)) {
    item_names_vec <- paste0("V", seq_len(ncol(data_mat)))
  }

  # Generating Rasch model: CML item thresholds (psychotools) + WLE person
  # locations, consistent with the rest of the package. The DGP is unchanged
  # (thetas resampled with replacement; data simulated under the model). The
  # CFA itself is fitted by lavaan; `estimator` is the lavaan estimator.
  pool <- .wle_theta_pool(data_mat)
  thr_list <- pool$thr_list
  wle_thetas <- pool$thetas

  sim_data_list <- list(
    type = if (is_polytomous) "polytomous" else "dichotomous",
    thetas = wle_thetas,
    n_items = ncol(data_mat),
    sample_n = sample_n,
    item_names = item_names_vec,
    estimator = estimator
  )
  if (is_polytomous) {
    sim_data_list$deltaslist <- thr_list
  } else {
    sim_data_list$item_params <- unlist(thr_list, use.names = FALSE)
  }

  # Run the simulation
  if (use_parallel) {
    results_raw <- run_cfa_sim_parallel(
      iterations,
      sim_seeds,
      sim_data_list,
      n_cores,
      verbose
    )
  } else {
    results_raw <- run_cfa_sim_sequential(
      iterations,
      sim_seeds,
      sim_data_list,
      verbose
    )
  }

  ok <- vapply(results_raw, is.list, logical(1L))
  successful <- results_raw[ok]

  if (length(successful) == 0L) {
    failed_msgs <- unlist(results_raw[!ok])
    sample_msg <- if (length(failed_msgs) > 0L) {
      unique(failed_msgs)[1L]
    } else {
      "(no message captured)"
    }
    stop(
      "All CFA simulation iterations failed. Example: ",
      sample_msg,
      call. = FALSE
    )
  }

  actual_iterations <- length(successful)

  fit_mat <- do.call(rbind, lapply(successful, `[[`, "fit"))
  load_mat <- do.call(rbind, lapply(successful, `[[`, "loadings"))
  colnames(fit_mat) <- c("cfi", "rmsea", "srmr")
  colnames(load_mat) <- item_names_vec

  simulated_df <- data.frame(
    iteration = seq_len(actual_iterations),
    cfi = as.numeric(fit_mat[, "cfi"]),
    rmsea = as.numeric(fit_mat[, "rmsea"]),
    srmr = as.numeric(fit_mat[, "srmr"]),
    stringsAsFactors = FALSE
  )

  simulated_loadings <- data.frame(
    iteration = seq_len(actual_iterations),
    as.data.frame(load_mat, check.names = FALSE),
    check.names = FALSE,
    stringsAsFactors = FALSE
  )

  cutoffs <- compute_cfa_cutoffs(simulated_df, percentile)
  loading_cutoffs <- compute_cfa_loading_cutoffs(
    simulated_loadings,
    item_names_vec,
    percentile
  )

  list(
    simulated = simulated_df,
    simulated_loadings = simulated_loadings,
    percentile = percentile,
    cutoffs = cutoffs,
    loading_cutoffs = loading_cutoffs,
    actual_iterations = actual_iterations,
    sample_n = sample_n,
    sample_n_total = n_total,
    sample_has_na = has_na,
    n_items = ncol(data_mat),
    item_names = item_names_vec,
    is_polytomous = is_polytomous,
    estimator = estimator
  )
}

# ===========================================================================
# Internal: cutoff / flag helpers
# ===========================================================================

#' Compute one-sided CFA-fit cutoffs at the given percentile
#'
#' @keywords internal
#' @noRd
compute_cfa_cutoffs <- function(simulated_df, percentile) {
  pct <- percentile / 100
  # Filter to finite values -- lavaan can return Inf for RMSEA when
  # chi-square is exactly 0 (degenerate near-perfect fit on a
  # simulated dataset), which would otherwise propagate into the cutoff.
  cfi_v <- simulated_df$cfi[is.finite(simulated_df$cfi)]
  rmsea_v <- simulated_df$rmsea[is.finite(simulated_df$rmsea)]
  srmr_v <- simulated_df$srmr[is.finite(simulated_df$srmr)]
  c(
    cfi = as.numeric(stats::quantile(cfi_v, 1 - pct, na.rm = TRUE)),
    rmsea = as.numeric(stats::quantile(rmsea_v, pct, na.rm = TRUE)),
    srmr = as.numeric(stats::quantile(srmr_v, pct, na.rm = TRUE))
  )
}

#' Compute two-sided per-item expected loading intervals
#'
#' Central interval covering `percentile`% of each item's simulated
#' standardized loadings (tails of `(100 - percentile) / 2`% each).
#'
#' @keywords internal
#' @noRd
compute_cfa_loading_cutoffs <- function(
  simulated_loadings,
  item_names,
  percentile
) {
  tail <- (1 - percentile / 100) / 2
  do.call(
    rbind,
    lapply(item_names, function(it) {
      v <- simulated_loadings[[it]]
      v <- v[is.finite(v)]
      data.frame(
        Item = it,
        low = as.numeric(stats::quantile(v, tail, na.rm = TRUE)),
        high = as.numeric(stats::quantile(v, 1 - tail, na.rm = TRUE)),
        stringsAsFactors = FALSE,
        row.names = NULL
      )
    })
  )
}

#' Tiny ordinal-suffix helper for percentile labels (1st, 2nd, 3rd, 99th)
#'
#' @keywords internal
#' @noRd
ordinal_suffix <- function(n) {
  n_int <- as.integer(round(n))
  if (n_int %% 100 %in% c(11L, 12L, 13L)) {
    return("th")
  }
  switch(as.character(n_int %% 10), "1" = "st", "2" = "nd", "3" = "rd", "th")
}

#' Apply directional fit-index flagging given observed values + cutoffs
#'
#' @keywords internal
#' @noRd
compute_cfa_flagged <- function(observed, cutoffs) {
  c(
    cfi = !is.na(observed[["cfi"]]) && observed[["cfi"]] < cutoffs[["cfi"]],
    rmsea = !is.na(observed[["rmsea"]]) &&
      observed[["rmsea"]] > cutoffs[["rmsea"]],
    srmr = !is.na(observed[["srmr"]]) && observed[["srmr"]] > cutoffs[["srmr"]]
  )
}

#' Flag per-item loadings against the two-sided expected interval
#'
#' Returns a character vector aligned to `loading_cutoffs$Item`:
#' `"below"` (observed loading under the expected interval),
#' `"above"` (over it), or `""` (within).
#'
#' @keywords internal
#' @noRd
compute_cfa_loading_flagged <- function(observed_loadings, loading_cutoffs) {
  obs <- as.numeric(observed_loadings[loading_cutoffs$Item])
  ifelse(
    is.na(obs),
    "",
    ifelse(
      obs < loading_cutoffs$low,
      "below",
      ifelse(obs > loading_cutoffs$high, "above", "")
    )
  )
}

# ===========================================================================
# Observed-vs-expected tables
# ===========================================================================

#' Observed one-factor CFA fit and loadings vs a simulated reference
#'
#' Fits the observed one-factor categorical CFA to `data` and compares its
#' fit indices and per-item standardized loadings against the simulated null
#' distribution produced by \code{\link{RMdimCFACutoff}}. Returns a list of
#' two tables: model-fit indices and per-item loadings, each with the observed
#' value, the expected reference from the simulation, and a flag.
#'
#' @param data A data.frame or matrix of item responses (non-negative
#'   integers, 0-based), the same items used for the cutoff simulation.
#' @param cutoff The list returned by \code{\link{RMdimCFACutoff}}. Required:
#'   observed CFA fit indices are not interpretable without the simulated
#'   reference, so the function errors if it is missing.
#' @param p_value Logical. When `TRUE`, adds bootstrap p-values from the
#'   simulated null distributions: one-sided in the unfavourable direction
#'   for the fit indices (CFI low; RMSEA / SRMR high), two-sided for the
#'   per-item loadings. The `Flagged` columns then reflect
#'   `padj < alpha` instead of the percentile cutoffs. The fit indices and
#'   the loadings are corrected as two separate families. Default `FALSE`.
#' @param correction Character. Multiplicity correction for the p-values:
#'   `"fwer"` (default; Westfall-Young studentised-max step-down), `"fdr_bh"`
#'   (Benjamini-Hochberg), `"fdr_by"` (Benjamini-Yekutieli), or `"none"`.
#'   Ignored when `p_value = FALSE`.
#' @param alpha Numeric in (0, 1). Significance level used to flag
#'   comparisons on the corrected p-value. Default `0.05`. Ignored when
#'   `p_value = FALSE`.
#' @param output Character. `"kable"` (default) returns each table as a
#'   `knitr::kable()`; `"dataframe"` returns plain data.frames.
#'
#' @return A named list with two elements, `fit` and `loadings`:
#' \describe{
#'   \item{`fit`}{CFI / RMSEA / SRMR with columns `Index`, `Observed`,
#'     `Cutoff`, `Direction`, `Flagged` (one-sided, in the unfavourable
#'     direction). With `p_value = TRUE`, columns `p` and `padj` are added
#'     and `Flagged` reflects `padj < alpha`.}
#'   \item{`loadings`}{One row per item with columns `Item`, `Observed`,
#'     `Expected_low`, `Expected_high`, `Flagged` (`"below"` / `"above"` /
#'     `""`). With `p_value = TRUE`, columns `p_loading` and `padj_loading`
#'     are added and `Flagged` reflects `padj_loading < alpha` (direction
#'     from the sign of the deviation from the simulated mean).}
#' }
#' Each element is a `knitr_kable` (when `output = "kable"`) or a data.frame
#' (when `output = "dataframe"`).
#'
#' @details
#' \strong{Bootstrap p-values.} The per-comparison statistic is the residual
#' studentised by the bootstrap mean and SD. Marginal p-values are Monte-Carlo,
#' `(1 + count) / (B + 1)`, so they can be no smaller than `1 / (B + 1)`.
#' `correction = "fwer"` uses the Westfall-Young studentised-max step-down,
#' which exploits the bootstrap dependence among the statistics (Ferreira,
#' 2024); it is liberal when the simulation is small, so at least 1000
#' `iterations` in [RMdimCFACutoff()] are recommended (a warning is issued
#' below that). These p-values are model-conditional and sample-size-sensitive
#' and are reported alongside the simulated expected ranges, not in place of
#' them.
#'
#' @inheritSection RMitemInfit Multiple comparisons
#'
#' @references
#' Ferreira, J. A. (2024). Methods of testing a 'small' or 'moderate' number
#' of hypotheses simultaneously. *Journal of Statistical Theory and Practice,
#' 19*(6). \doi{10.1007/s42519-024-00412-4}
#'
#' Westfall, P. H., & Young, S. S. (1993). *Resampling-Based Multiple Testing*.
#' Wiley.
#'
#' @seealso \code{\link{RMdimCFACutoff}}, \code{\link{RMdimCFAPlot}}
#'
#' @examples
#' \donttest{
#' if (requireNamespace("lavaan", quietly = TRUE) &&
#'     requireNamespace("eRm", quietly = TRUE)) {
#'   data("raschdat1", package = "eRm")
#'   sim <- RMdimCFACutoff(raschdat1[, 1:8], iterations = 50,
#'                         parallel = FALSE, seed = 1)
#'   tabs <- RMdimCFA(raschdat1[, 1:8], cutoff = sim)
#'   tabs$fit
#'   tabs$loadings
#'
#'   # Bootstrap p-values with family-wise (Westfall-Young) correction
#'   # (use iterations >= 1000 in real analyses for stable p-values)
#'   RMdimCFA(raschdat1[, 1:8], cutoff = sim, p_value = TRUE)
#' }
#' }
#'
#' @export
RMdimCFA <- function(
  data,
  cutoff,
  p_value = FALSE,
  correction = c("fwer", "fdr_bh", "fdr_by", "none"),
  alpha = 0.05,
  output = c("kable", "dataframe")
) {
  output <- match.arg(output)
  correction <- match.arg(correction)
  if (!is.numeric(alpha) || length(alpha) != 1L || alpha <= 0 || alpha >= 1) {
    stop("`alpha` must be a single number in (0, 1).", call. = FALSE)
  }

  if (missing(cutoff) || is.null(cutoff)) {
    stop(
      "`cutoff` is required: pass the object returned by RMdimCFACutoff(). ",
      "Observed CFA fit indices are not interpretable without the ",
      "simulated reference distribution.",
      call. = FALSE
    )
  }
  req <- c(
    "simulated",
    "simulated_loadings",
    "cutoffs",
    "loading_cutoffs",
    "percentile",
    "item_names",
    "estimator"
  )
  if (!is.list(cutoff) || !all(req %in% names(cutoff))) {
    stop(
      "`cutoff` must be the list returned by RMdimCFACutoff().",
      call. = FALSE
    )
  }

  # --- p-value prerequisites --------------------------------------------------
  # The full cutoff object always carries $simulated / $simulated_loadings
  # (checked above), so only the iteration count needs a warning.
  if (
    p_value &&
      !is.null(cutoff$actual_iterations) &&
      cutoff$actual_iterations < 1000L
  ) {
    warning(
      "Bootstrap p-values are based on only ",
      cutoff$actual_iterations,
      " simulation iterations. With few iterations the studentised-max ",
      "(FWER) correction is liberal and small p-values are imprecise; ",
      "use iterations >= 1000 in RMdimCFACutoff() for reliable p-values.",
      call. = FALSE
    )
  }

  if (!requireNamespace("lavaan", quietly = TRUE)) {
    stop(
      "Package 'lavaan' is required for RMdimCFA(). ",
      "Install with: install.packages(\"lavaan\")",
      call. = FALSE
    )
  }
  if (output == "kable" && !requireNamespace("knitr", quietly = TRUE)) {
    stop("Package 'knitr' is required for output = \"kable\".", call. = FALSE)
  }

  validate_response_data(data)

  old_rgl <- getOption("rgl.useNULL")
  options(rgl.useNULL = TRUE)
  on.exit(options(rgl.useNULL = old_rgl), add = TRUE)

  data <- as.data.frame(data)
  n_total_cfa <- nrow(data)
  has_na_cfa <- anyNA(data)
  data <- stats::na.omit(data)
  if (nrow(data) == 0L) {
    stop("No complete cases in `data`.", call. = FALSE)
  }
  n_clause_cfa <- .n_caption(
    nrow(data),
    n_total_cfa,
    if (has_na_cfa) "complete cases" else character()
  )

  item_names <- cutoff$item_names
  if (!setequal(names(data), item_names)) {
    stop(
      "Item names in `data` do not match those used for the cutoff.\n",
      "  data   : ",
      paste(names(data), collapse = ", "),
      "\n",
      "  cutoff : ",
      paste(item_names, collapse = ", "),
      call. = FALSE
    )
  }
  data <- data[, item_names, drop = FALSE]

  # Observed CFA fit + standardized loadings (one lavaan fit)
  obs <- run_single_cfa_sim(
    seed = NA,
    data_list = list(item_names = item_names, estimator = cutoff$estimator),
    obs_data = data
  )
  if (!is.list(obs)) {
    stop("Observed CFA fit failed: ", obs, call. = FALSE)
  }
  observed_fit <- stats::setNames(obs$fit, c("cfi", "rmsea", "srmr"))
  observed_load <- obs$loadings

  flagged_fit <- compute_cfa_flagged(observed_fit, cutoff$cutoffs)
  loading_flagged <- compute_cfa_loading_flagged(
    observed_load,
    cutoff$loading_cutoffs
  )

  fit_df <- build_cfa_fit_df(
    observed_fit,
    cutoff$cutoffs,
    flagged_fit,
    cutoff$percentile
  )
  load_df <- build_cfa_loadings_df(
    observed_load,
    cutoff$loading_cutoffs,
    loading_flagged
  )

  # --- Bootstrap p-values (two separate families: fit indices, loadings) ------
  if (p_value) {
    # Fit indices: one-sided in the unfavourable direction. CFI is negated so
    # "large = bad" holds for all three; RMSEA can be Inf on a degenerate
    # simulated dataset, so non-finite simulated values are dropped to NA.
    sim_fit <- cbind(
      CFI = -cutoff$simulated$cfi,
      RMSEA = cutoff$simulated$rmsea,
      SRMR = cutoff$simulated$srmr
    )
    sim_fit[!is.finite(sim_fit)] <- NA_real_
    obs_fit_stat <- c(
      CFI = -observed_fit[["cfi"]],
      RMSEA = observed_fit[["rmsea"]],
      SRMR = observed_fit[["srmr"]]
    )
    pv_fit <- .bootstrap_pvalues(
      obs_fit_stat,
      sim_fit,
      correction = correction,
      tail = "upper"
    )
    fit_df$p <- pv_fit$p
    fit_df$padj <- pv_fit$padj
    fit_df$Flagged <- ifelse(
      !is.na(fit_df$padj) & fit_df$padj < alpha,
      "TRUE",
      ""
    )
    fit_df <- fit_df[, c(
      "Index",
      "Observed",
      "Cutoff",
      "Direction",
      "p",
      "padj",
      "Flagged"
    )]

    # Loadings: two-sided per item; the flag direction comes from the sign of
    # the deviation from the simulated mean loading.
    sim_load <- as.matrix(cutoff$simulated_loadings[, item_names, drop = FALSE])
    sim_load[!is.finite(sim_load)] <- NA_real_
    obs_load_vec <- stats::setNames(
      as.numeric(observed_load[item_names]),
      item_names
    )
    pv_load <- .bootstrap_pvalues(
      obs_load_vec,
      sim_load,
      correction = correction,
      tail = "two.sided"
    )
    idx <- match(load_df$Item, pv_load$name)
    load_df$p_loading <- pv_load$p[idx]
    load_df$padj_loading <- pv_load$padj[idx]
    sim_mean <- colMeans(sim_load, na.rm = TRUE)[load_df$Item]
    sig <- !is.na(load_df$padj_loading) & load_df$padj_loading < alpha
    load_df$Flagged <- ifelse(
      sig,
      ifelse(load_df$Observed < sim_mean, "below", "above"),
      ""
    )
    load_df <- load_df[, c(
      "Item",
      "Observed",
      "Expected_low",
      "Expected_high",
      "p_loading",
      "padj_loading",
      "Flagged"
    )]
  }

  if (output == "dataframe") {
    return(list(fit = fit_df, loadings = load_df))
  }

  # Kable display rounding (the dataframe output above stays unrounded)
  fit_df <- .round_display(fit_df, c(Observed = 4, Cutoff = 4, p = 4, padj = 4))
  load_df <- .round_display(load_df, c(
    Observed = 3, Expected_low = 3, Expected_high = 3,
    p_loading = 4, padj_loading = 4
  ))

  list(
    fit = render_cfa_fit_kable(
      fit_df,
      cutoff,
      n_clause_cfa,
      p_value = p_value,
      correction = correction,
      alpha = alpha
    ),
    loadings = render_cfa_loadings_kable(
      load_df,
      cutoff,
      n_clause_cfa,
      p_value = p_value,
      correction = correction,
      alpha = alpha
    )
  )
}

#' Assemble the fit-index data.frame (Index/Observed/Cutoff/Direction/Flagged)
#'
#' @keywords internal
#' @noRd
build_cfa_fit_df <- function(observed, cutoffs, flagged, percentile) {
  cfi_pct_lbl <- 100 - percentile
  pct_suffix <- ordinal_suffix(percentile)
  cfi_suffix <- ordinal_suffix(cfi_pct_lbl)
  data.frame(
    Index = c("CFI", "RMSEA", "SRMR"),
    Observed = c(observed[["cfi"]], observed[["rmsea"]], observed[["srmr"]]),
    Cutoff = c(cutoffs[["cfi"]], cutoffs[["rmsea"]], cutoffs[["srmr"]]),
    Direction = c(
      paste0("< ", cfi_pct_lbl, cfi_suffix, " pct"),
      paste0("> ", percentile, pct_suffix, " pct"),
      paste0("> ", percentile, pct_suffix, " pct")
    ),
    Flagged = ifelse(flagged[c("cfi", "rmsea", "srmr")], "TRUE", ""),
    stringsAsFactors = FALSE,
    row.names = NULL
  )
}

#' Assemble the loadings data.frame (Item/Observed/Expected_low/high/Flagged)
#'
#' @keywords internal
#' @noRd
build_cfa_loadings_df <- function(
  observed_loadings,
  loading_cutoffs,
  loading_flagged
) {
  data.frame(
    Item = loading_cutoffs$Item,
    Observed = as.numeric(observed_loadings[loading_cutoffs$Item]),
    Expected_low = loading_cutoffs$low,
    Expected_high = loading_cutoffs$high,
    Flagged = loading_flagged,
    stringsAsFactors = FALSE,
    row.names = NULL
  )
}

#' @keywords internal
#' @noRd
render_cfa_fit_kable <- function(
  fit_df,
  cutoff,
  n_clause,
  p_value = FALSE,
  correction = "fwer",
  alpha = 0.05
) {
  pct_label <- cutoff$percentile
  caption <- paste0(
    if (cutoff$is_polytomous) "Partial Credit Model" else "Rasch Model",
    " posterior-predictive CFA fit-index check. ",
    "Observed CFA fit (one-factor, lavaan ",
    cutoff$estimator,
    ", ordered = TRUE) vs simulated null under ",
    if (cutoff$is_polytomous) "PCM" else "RM",
    " unidimensionality (",
    cutoff$actual_iterations,
    " iterations at n = ",
    cutoff$sample_n,
    " simulees). ",
    n_clause,
    if (p_value) {
      paste0(
        ". Cutoffs shown at the ",
        pct_label,
        "th percentile for reference. One-sided bootstrap p-values in the ",
        "unfavourable direction (CFI low; RMSEA/SRMR high); multiplicity ",
        "correction: ",
        .correction_label(correction),
        "; flagged at padj < ",
        alpha,
        ". p-values cannot be smaller than 1/(",
        cutoff$actual_iterations,
        "+1) = ",
        round(1 / (cutoff$actual_iterations + 1), 4),
        "."
      )
    } else {
      paste0(
        ". Cutoffs one-sided at the ",
        pct_label,
        "th percentile; flagged when the observed value lies in the worst ",
        100 - pct_label,
        "% of the null in the unfavourable direction."
      )
    }
  )
  knitr::kable(
    fit_df,
    format = "pipe",
    caption = caption,
    row.names = FALSE,
    col.names = if (p_value) {
      c("Index", "Observed", "Cutoff", "Direction", "p", "p (adj)", "Flagged")
    } else {
      names(fit_df)
    }
  )
}

#' @keywords internal
#' @noRd
render_cfa_loadings_kable <- function(
  load_df,
  cutoff,
  n_clause,
  p_value = FALSE,
  correction = "fwer",
  alpha = 0.05
) {
  pct_label <- cutoff$percentile
  caption <- paste0(
    "Standardized factor loadings (one-factor, lavaan ",
    cutoff$estimator,
    ") vs the simulated expected range under ",
    if (cutoff$is_polytomous) "PCM" else "RM",
    " unidimensionality (",
    cutoff$actual_iterations,
    " iterations at n = ",
    cutoff$sample_n,
    " simulees). ",
    n_clause,
    if (p_value) {
      paste0(
        ". Expected range shown for reference (two-sided central ",
        pct_label,
        "% interval). p/p (adj): two-sided bootstrap p-values; multiplicity ",
        "correction: ",
        .correction_label(correction),
        "; flagged at padj < ",
        alpha,
        " (below / above = direction of the deviation)."
      )
    } else {
      paste0(
        ". Expected range is the two-sided central ",
        pct_label,
        "% interval of the simulated loadings; Flagged = below / above that range."
      )
    }
  )
  knitr::kable(
    load_df,
    format = "pipe",
    row.names = FALSE,
    col.names = if (p_value) {
      c(
        "Item",
        "Observed",
        "Expected low",
        "Expected high",
        "p",
        "p (adj)",
        "Flagged"
      )
    } else {
      c(
        "Item",
        "Observed",
        "Expected low",
        "Expected high",
        "Flagged"
      )
    },
    caption = caption
  )
}

# ===========================================================================
# Plot companion
# ===========================================================================

#' Plot observed CFA fit and loadings against the simulated null
#'
#' Returns two figures (in a list) comparing the observed one-factor CFA to
#' the simulated null distribution from \code{\link{RMdimCFACutoff}}:
#' a per-item standardized-loadings plot (observed marker against each item's
#' simulated distribution and expected range, in the style of
#' \code{\link{RMitemInfitPlot}}), and a faceted plot of the CFI / RMSEA
#' / SRMR distributions with the observed value overlaid.
#'
#' @param simfit The list returned by \code{\link{RMdimCFACutoff}}.
#' @param data The item-response data the CFA was run on (the same items used
#'   for the cutoff). Required: the observed values are computed from it.
#' @param percentile Numeric in (50, 100) or `NULL`. When supplied, the
#'   cutoffs and flags are recomputed at this percentile from the stored
#'   simulated distributions (no re-simulation). When `NULL` (default), the
#'   percentile from the original `RMdimCFACutoff()` call is reused.
#'
#' @return A named list of two `ggplot` objects:
#' \describe{
#'   \item{`loadings`}{Per-item standardized loadings: simulated distribution
#'     (dots), expected interval, and the observed loading as a diamond
#'     (red when flagged).}
#'   \item{`fit`}{Faceted CFI / RMSEA / SRMR simulated distributions with the
#'     observed value and cutoff overlaid.}
#' }
#'
#' @seealso \code{\link{RMdimCFACutoff}}, \code{\link{RMdimCFA}}
#'
#' @examples
#' \donttest{
#' if (requireNamespace("lavaan", quietly = TRUE) &&
#'     requireNamespace("ggplot2", quietly = TRUE) &&
#'     requireNamespace("eRm", quietly = TRUE)) {
#'   data("raschdat1", package = "eRm")
#'   sim <- RMdimCFACutoff(raschdat1[, 1:8], iterations = 50,
#'                         parallel = FALSE, seed = 1)
#'   plots <- RMdimCFAPlot(sim, data = raschdat1[, 1:8])
#'   plots$loadings
#'   plots$fit
#' }
#' }
#'
#' @importFrom rlang .data
#' @export
RMdimCFAPlot <- function(simfit, data, percentile = NULL) {
  if (!requireNamespace("ggplot2", quietly = TRUE)) {
    stop(
      "Package 'ggplot2' is required for RMdimCFAPlot(). ",
      "Install with: install.packages(\"ggplot2\")",
      call. = FALSE
    )
  }
  if (!requireNamespace("ggdist", quietly = TRUE)) {
    stop(
      "Package 'ggdist' is required for RMdimCFAPlot(). ",
      "Install with: install.packages(\"ggdist\")",
      call. = FALSE
    )
  }

  req <- c(
    "simulated",
    "simulated_loadings",
    "cutoffs",
    "loading_cutoffs",
    "percentile",
    "item_names",
    "estimator"
  )
  if (!is.list(simfit) || !all(req %in% names(simfit))) {
    stop(
      "`simfit` must be the result returned by RMdimCFACutoff().",
      call. = FALSE
    )
  }
  if (missing(data)) {
    stop(
      "`data` is required: the observed CFA fit and loadings are computed ",
      "from it for the overlay.",
      call. = FALSE
    )
  }

  # Resolve / override percentile
  if (is.null(percentile)) {
    percentile <- simfit$percentile
  } else if (
    !is.numeric(percentile) ||
      length(percentile) != 1L ||
      !is.finite(percentile) ||
      percentile <= 50 ||
      percentile >= 100
  ) {
    stop(
      "`percentile` must be a single numeric in (50, 100), or NULL.",
      call. = FALSE
    )
  }
  cutoffs <- compute_cfa_cutoffs(simfit$simulated, percentile)
  loading_cutoffs <- compute_cfa_loading_cutoffs(
    simfit$simulated_loadings,
    simfit$item_names,
    percentile
  )

  # Observed fit + loadings from the data
  validate_response_data(data)
  data <- stats::na.omit(as.data.frame(data))
  if (!setequal(names(data), simfit$item_names)) {
    stop(
      "Item names in `data` do not match those used for the cutoff.",
      call. = FALSE
    )
  }
  data <- data[, simfit$item_names, drop = FALSE]

  old_rgl <- getOption("rgl.useNULL")
  options(rgl.useNULL = TRUE)
  on.exit(options(rgl.useNULL = old_rgl), add = TRUE)

  obs <- run_single_cfa_sim(
    seed = NA,
    data_list = list(
      item_names = simfit$item_names,
      estimator = simfit$estimator
    ),
    obs_data = data
  )
  if (!is.list(obs)) {
    stop("Observed CFA fit failed: ", obs, call. = FALSE)
  }
  observed_fit <- stats::setNames(obs$fit, c("cfi", "rmsea", "srmr"))
  observed_load <- obs$loadings

  list(
    loadings = cfa_loadings_plot(
      simfit,
      observed_load,
      loading_cutoffs,
      percentile
    ),
    fit = cfa_fit_plot(simfit, observed_fit, cutoffs, percentile)
  )
}

#' Standard sample-size clause for the CFA plot captions
#'
#' Reports the (complete-case) simulation sample in the standard
#' `n = X of Y respondents (policy)` form. `sample_n_total` /
#' `sample_has_na` are absent in cutoff objects made by older versions,
#' so fall back to the plain count.
#'
#' @keywords internal
#' @noRd
.cfa_sample_clause <- function(simfit) {
  .n_caption(
    simfit$sample_n,
    if (is.null(simfit$sample_n_total)) {
      simfit$sample_n
    } else {
      simfit$sample_n_total
    },
    if (isTRUE(simfit$sample_has_na)) "complete cases" else character()
  )
}

#' Per-item standardized-loadings plot (RMitemInfitPlot style)
#'
#' @keywords internal
#' @noRd
cfa_loadings_plot <- function(
  simfit,
  observed_load,
  loading_cutoffs,
  percentile
) {
  item_names <- simfit$item_names
  item_levels <- rev(item_names)
  sim_load <- simfit$simulated_loadings

  # Long-format simulated loadings
  sim_long <- do.call(
    rbind,
    lapply(item_names, function(it) {
      data.frame(
        Item = it,
        Value = sim_load[[it]],
        stringsAsFactors = FALSE,
        row.names = NULL
      )
    })
  )
  sim_long$Item <- factor(sim_long$Item, levels = item_levels)

  # Per-item summary intervals (thin = 0.1/99.9, thick = 16.7/83.3, median)
  lo_hi <- do.call(
    rbind,
    lapply(item_names, function(it) {
      v <- sim_load[[it]]
      v <- v[is.finite(v)]
      data.frame(
        Item = it,
        lo = stats::quantile(v, 0.001, na.rm = TRUE),
        hi = stats::quantile(v, 0.999, na.rm = TRUE),
        lo66 = stats::quantile(v, 0.167, na.rm = TRUE),
        hi66 = stats::quantile(v, 0.833, na.rm = TRUE),
        median = stats::median(v, na.rm = TRUE),
        stringsAsFactors = FALSE,
        row.names = NULL
      )
    })
  )
  lo_hi$Item_f <- factor(lo_hi$Item, levels = item_levels)

  flags <- compute_cfa_loading_flagged(observed_load, loading_cutoffs)
  obs_df <- data.frame(
    Item = loading_cutoffs$Item,
    observed = as.numeric(observed_load[loading_cutoffs$Item]),
    Flagged = flags != "",
    stringsAsFactors = FALSE
  )
  obs_df$Item <- factor(obs_df$Item, levels = item_levels)
  obs_df$Color <- ifelse(obs_df$Flagged, "red", "sienna2")

  caption_text <- er2_caption(paste0(
    "Standardized factor loadings from ",
    simfit$actual_iterations,
    " datasets simulated under ",
    if (simfit$is_polytomous) "PCM" else "RM",
    " unidimensionality at ",
    .cfa_sample_clause(simfit),
    " per dataset, refitted with lavaan::cfa(ordered = TRUE, estimator = \"",
    simfit$estimator,
    "\").\n",
    "Diamonds: observed loading (red = outside the two-sided ",
    percentile,
    "% expected range). Black dots: simulated median."
  ))

  ggplot2::ggplot(sim_long, ggplot2::aes(x = .data$Value, y = .data$Item)) +
    ggdist::stat_dots(
      ggplot2::aes(slab_fill = ggplot2::after_stat(.data$level)),
      quantiles = simfit$actual_iterations,
      layout = "weave",
      slab_color = NA,
      .width = c(0.666, 0.999)
    ) +
    ggplot2::geom_segment(
      data = lo_hi,
      ggplot2::aes(
        x = .data$lo,
        xend = .data$hi,
        y = .data$Item_f,
        yend = .data$Item_f
      ),
      color = "black",
      linewidth = 0.7
    ) +
    ggplot2::geom_segment(
      data = lo_hi,
      ggplot2::aes(
        x = .data$lo66,
        xend = .data$hi66,
        y = .data$Item_f,
        yend = .data$Item_f
      ),
      color = "black",
      linewidth = 1.2
    ) +
    ggplot2::geom_point(
      data = lo_hi,
      ggplot2::aes(x = .data$median, y = .data$Item_f),
      size = 3.6
    ) +
    ggplot2::geom_point(
      data = obs_df,
      ggplot2::aes(x = .data$observed, y = .data$Item, colour = .data$Color),
      shape = 18,
      size = 4,
      position = ggplot2::position_nudge(y = -0.1)
    ) +
    ggplot2::scale_colour_identity() +
    ggplot2::scale_color_manual(
      values = scales::brewer_pal()(3)[-1],
      aesthetics = "slab_fill",
      guide = "none"
    ) +
    ggplot2::labs(
      x = "Standardized factor loading",
      y = "Item",
      caption = caption_text
    ) +
    ggplot2::theme_minimal() +
    ggplot2::theme(panel.spacing = ggplot2::unit(0.7, "cm")) +
    er2_axis_margins() +
    er2_plot_caption()
}

#' Faceted CFI / RMSEA / SRMR distribution plot with observed overlay
#'
#' @keywords internal
#' @noRd
cfa_fit_plot <- function(simfit, observed_fit, cutoffs, percentile) {
  flagged <- compute_cfa_flagged(observed_fit, cutoffs)

  sim_long <- data.frame(
    Index = factor(
      rep(c("CFI", "RMSEA", "SRMR"), each = nrow(simfit$simulated)),
      levels = c("CFI", "RMSEA", "SRMR")
    ),
    Value = c(
      simfit$simulated$cfi,
      simfit$simulated$rmsea,
      simfit$simulated$srmr
    ),
    stringsAsFactors = FALSE
  )

  obs_df <- data.frame(
    Index = factor(
      c("CFI", "RMSEA", "SRMR"),
      levels = c("CFI", "RMSEA", "SRMR")
    ),
    Observed = c(
      observed_fit[["cfi"]],
      observed_fit[["rmsea"]],
      observed_fit[["srmr"]]
    ),
    Cutoff = c(cutoffs[["cfi"]], cutoffs[["rmsea"]], cutoffs[["srmr"]]),
    Flagged = c(flagged[["cfi"]], flagged[["rmsea"]], flagged[["srmr"]]),
    stringsAsFactors = FALSE
  )
  obs_df$Color <- ifelse(obs_df$Flagged, "red", "grey30")

  cfi_pct_lbl <- 100 - percentile
  caption <- er2_caption(paste0(
    "Histograms: ",
    simfit$actual_iterations,
    " datasets simulated under ",
    if (simfit$is_polytomous) "PCM" else "RM",
    " unidimensionality at ",
    .cfa_sample_clause(simfit),
    " per dataset,\nrefitted with lavaan::cfa(ordered = TRUE, estimator = \"",
    simfit$estimator,
    "\").\n",
    "Diamond: observed value (red = flagged at the ",
    percentile,
    "th percentile in the unfavourable direction).\n",
    "Dashed line: cutoff (CFI: ",
    cfi_pct_lbl,
    "th pct; RMSEA / SRMR: ",
    percentile,
    "th pct)."
  ))

  ggplot2::ggplot(sim_long, ggplot2::aes(x = .data$Value)) +
    ggplot2::geom_histogram(bins = 30, fill = "grey80", colour = "white") +
    ggplot2::geom_vline(
      data = obs_df,
      ggplot2::aes(xintercept = .data$Cutoff),
      linetype = "dashed",
      colour = "grey40"
    ) +
    ggplot2::geom_point(
      data = obs_df,
      ggplot2::aes(x = .data$Observed, colour = .data$Color),
      y = 0,
      size = 4.5,
      shape = 18
    ) +
    ggplot2::scale_colour_identity() +
    ggplot2::facet_wrap(~Index, scales = "free", nrow = 1) +
    ggplot2::labs(
      title = "Observed CFA fit vs simulated null distribution",
      x = "Fit index value",
      y = "Count of simulated datasets",
      caption = caption
    ) +
    ggplot2::theme_bw(base_size = 13) +
    er2_axis_margins() +
    er2_plot_caption()
}

# ===========================================================================
# Internal: single iteration
# ===========================================================================

#' Run one CFA simulation iteration
#'
#' If `obs_data` is supplied, it is used directly (the observed-fit call);
#' otherwise a new dataset is simulated under the fitted PCM/RM. Returns a
#' list `list(fit = c(cfi, rmsea, srmr), loadings = <named vector>)` on
#' success, or a character message on failure.
#'
#' @keywords internal
#' @noRd
run_single_cfa_sim <- function(seed, data_list, obs_data = NULL) {
  build_sim_df <- function() {
    set.seed(seed)
    thetas_res <- sample(
      data_list$thetas,
      size = data_list$sample_n,
      replace = TRUE
    )

    if (data_list$type == "dichotomous") {
      sim_mat <- psychotools::rrm(
        theta = thetas_res,
        beta = data_list$item_params
      )
      sim_df <- as.data.frame(sim_mat$data)
      colnames(sim_df) <- data_list$item_names

      pos_counts <- colSums(sim_df, na.rm = TRUE)
      neg_counts <- nrow(sim_df) - pos_counts
      if (any(pos_counts < 2L) || any(neg_counts < 2L)) {
        return(
          "validation_failed: an item has < 2 responses in one of the two categories"
        )
      }
      return(sim_df)
    }

    sim_mat <- sim_partial_score(data_list$deltaslist, thetas_res)
    sim_df <- as.data.frame(sim_mat)
    colnames(sim_df) <- data_list$item_names

    n_cats <- vapply(
      data_list$deltaslist,
      function(d) length(d) + 1L,
      integer(1L)
    )
    for (j in seq_len(ncol(sim_df))) {
      tab <- tabulate(sim_df[[j]] + 1L, nbins = n_cats[j])
      if (any(tab == 0L)) {
        return("validation_failed: not all categories represented")
      }
    }
    sim_df
  }

  tryCatch(
    {
      fit_df <- if (is.null(obs_data)) build_sim_df() else obs_data
      if (is.character(fit_df)) {
        return(fit_df)
      }

      fmla <- paste0("F1 =~ ", paste(data_list$item_names, collapse = " + "))

      fit <- suppressWarnings(suppressMessages(
        lavaan::cfa(
          model = fmla,
          data = fit_df,
          ordered = data_list$item_names,
          estimator = data_list$estimator,
          warn = FALSE,
          verbose = FALSE
        )
      ))

      if (!isTRUE(lavaan::lavInspect(fit, "converged"))) {
        return("convergence_failed: lavaan did not converge")
      }

      suppressWarnings(list(
        fit = extract_cfa_fit(fit, data_list$estimator),
        loadings = extract_cfa_loadings(fit, data_list$item_names)
      ))
    },
    error = function(e) as.character(conditionMessage(e))
  )
}

#' Pull (CFI, RMSEA, SRMR) from a fitted lavaan object
#'
#' Uses the Satorra-Bentler-SCALED CFI / RMSEA variants when available
#' (`cfi.scaled`, `rmsea.scaled`), falling back to the uncorrected
#' `cfi` / `rmsea` when not. SRMR is unaffected by the correction. The
#' `.scaled` variants are well-defined across nearly all fits (unlike the
#' `.robust` variants, which return `NA` for a non-trivial fraction at small
#' n); for a percentile comparison the binding requirement is that the same
#' metric is computed for observed and simulated iterations.
#'
#' @keywords internal
#' @noRd
extract_cfa_fit <- function(fit, estimator = NULL) {
  fm <- lavaan::fitMeasures(fit)
  pick_scaled <- function(name) {
    key_s <- paste0(name, ".scaled")
    if (key_s %in% names(fm) && is.finite(fm[[key_s]])) {
      return(as.numeric(fm[[key_s]]))
    }
    if (name %in% names(fm) && is.finite(fm[[name]])) {
      return(as.numeric(fm[[name]]))
    }
    NA_real_
  }
  c(pick_scaled("cfi"), pick_scaled("rmsea"), as.numeric(fm[["srmr"]]))
}

#' Pull standardized factor loadings (one factor) from a fitted lavaan object
#'
#' Returns the `est.std` of the `=~` paths from
#' `lavaan::standardizedSolution()`, aligned to `item_names`.
#'
#' @keywords internal
#' @noRd
extract_cfa_loadings <- function(fit, item_names) {
  ss <- lavaan::standardizedSolution(fit)
  lam <- ss[ss$op == "=~", , drop = FALSE]
  v <- stats::setNames(as.numeric(lam$est.std), lam$rhs)
  # Keep names so callers can index by item (observed-side lookups rely on it).
  stats::setNames(as.numeric(v[item_names]), item_names)
}

# ===========================================================================
# Internal: parallel runner
# ===========================================================================

#' @keywords internal
#' @noRd
run_cfa_sim_parallel <- function(
  iterations,
  sim_seeds,
  sim_data_list,
  n_cores,
  verbose = FALSE
) {
  mirai::daemons(n_cores)
  on.exit(mirai::daemons(0), add = TRUE)

  if (verbose) {
    message(sprintf("Starting %d daemons...", n_cores))
    pb <- utils::txtProgressBar(min = 0, max = iterations, style = 3)
  }

  tasks <- lapply(seq_len(iterations), function(i) {
    mirai::mirai(
      {
        run_single_cfa_sim(seed, data_list)
      },
      seed = sim_seeds[i],
      data_list = sim_data_list,
      run_single_cfa_sim = run_single_cfa_sim,
      extract_cfa_fit = extract_cfa_fit,
      extract_cfa_loadings = extract_cfa_loadings,
      sim_partial_score = sim_partial_score,
      sim_poly_item = sim_poly_item
    )
  })

  results <- vector("list", iterations)
  for (i in seq_len(iterations)) {
    res <- mirai::call_mirai(tasks[[i]])$data
    results[[i]] <- if (inherits(res, "errorValue")) "mirai_error" else res
    if (verbose) utils::setTxtProgressBar(pb, i)
  }

  if (verbose) {
    close(pb)
    message("")
  }

  results
}

# ===========================================================================
# Internal: sequential runner
# ===========================================================================

#' @keywords internal
#' @noRd
run_cfa_sim_sequential <- function(
  iterations,
  sim_seeds,
  sim_data_list,
  verbose = FALSE
) {
  if (verbose) {
    pb <- utils::txtProgressBar(min = 0, max = iterations, style = 3)
  }

  results <- vector("list", iterations)
  for (i in seq_len(iterations)) {
    results[[i]] <- run_single_cfa_sim(sim_seeds[i], sim_data_list)
    if (verbose) utils::setTxtProgressBar(pb, i)
  }

  if (verbose) {
    close(pb)
    message("")
  }

  results
}
