17: vapply(X = X, FUN = FUN, FUN.VALUE = numeric(LENGTH), ..., USE.NAMES = USE.NAMES)
16: nvapply(seq_len(max(convert_keep)), function(community) {
        community_index <- convert_keep == community
        total_indices <- sum(community_index)
        target_length <- unique_length(target_keep[community_index])
        convert_length <- unique_length(convert_keep[community_index])
        if (total_indices < 3) {
            return(0)
        }
        else if (target_length == total_indices || convert_length ==
            total_indices) {
            return(0)
        }
        else if ((target_length * convert_length) == 1) {
            return(1)
        }
        else {
            return(igraph::compare(target_keep[community_index],
                convert_keep[community_index], method = "rand"))
        }
    })
15: get_rand(convert_keep, target_keep)
14: order(get_rand(convert_keep, target_keep), rowSums(conversion_table),
        decreasing = TRUE)
13: FUN(X[[i]], ...)
12: vapply(X = X, FUN = FUN, FUN.VALUE = numeric(LENGTH), ..., USE.NAMES = USE.NAMES)
11: vapply_FUN(split(X, seq_len(dimensions[1])), FUN = FUN, ...,
        LENGTH = dimensions[2], USE.NAMES = FALSE)
10: matrix(vapply_FUN(split(X, seq_len(dimensions[1])), FUN = FUN,
        ..., LENGTH = dimensions[2], USE.NAMES = FALSE), dimensions,
        dimnames = dimnames(X), byrow = TRUE)
9: row_apply(convert.membership[numeric_ID[unique(names(numeric_ID))],
       , drop = FALSE], single_homogenize, target.membership = target.membership)
8: community.homogenize(target.membership = structure, convert.membership = bootstrap_structure)
7: itemStability_core(ega_object, structure, bootega.obj$boot.wc,
       bootega.obj$iter)
6: itemStability(bootega.obj, IS.plot, structure, ...)
5: (function (bootega.obj, IS.plot = TRUE, structure = NULL, ...)
   {
       item_stability <- itemStability(bootega.obj, IS.plot, structure,
           ...)
       if ("lower_order" %in% names(item_stability)) {
           results <- list(lower_order = dimensionStability_core(item_stability$lower_order),
               higher_order = dimensionStability_core(item_stability$higher_order),
               item.stability = item_stability)
       }
       else {
           results <- dimensionStability_core(item_stability)
       }
       class(results) <- "dimensionStability"
       return(results)
   })(bootega.obj = structure(list(iter = 500, bootGraphs = list(
       structure(c(0, 0, 0, 0, 0.179159576874653, 0.0155534584196898,
       0.0543115970881304, 0.199328891470066, 0, 0, 0, 0.287788161128426,
       0, 0, 0, 0.533613844183926, 0, 0.0494739132013045, 0.0500964965985357,
       0.18043803970601, 0.0220020606114242, 0.00575138764840636,
       0, 0.0779677917481022, 0, 0, 0, 0.106008408982162, 0.173526837639143,
    ...
4: do.call(what = dimensionStability, args = obtain_arguments(dimensionStability,
       FUN.args = c(list(bootega.obj = results, IS.plot = FALSE),
           ellipse)))
3: bootEGA(data = data, corr = corr, na.data = na.data, model = model,
       algorithm = algorithm, uni.method = uni.method, iter = iter,
       type = configural.type, ncores = ncores, EGA.type = "EGA",
       typicalStructure = FALSE, plot.itemStability = FALSE, plot.typicalStructure = FALSE,
       seed = seed, verbose = verbose, clear = TRUE, suppress = TRUE,
       ...)
2: configural(data = data, iter = iter, structure = structure, configural.threshold = configural.threshold,
       configural.type = configural.type, corr = corr, na.data = na.data,
       model = model, algorithm = algorithm, uni.method = uni.method,
       ncores = ncores, seed = seed, verbose = verbose, ...)
1: EGAnet::invariance(data = dataeg[grepl("^v\\d+", names(dataeg))],
       groups = dataeg$grupo, seed = 1245, na.data = "pairwise",
       corr = "spearman", ncores = 8)