diff --git a/NEWS.md b/NEWS.md
index 92292f4a..68aeebd8 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,6 +1,10 @@
 # BGmisc NEWS
 # Development version: 1.6.0.9000
 
+Add option for MZ twins in the additive genetic matrix
+Add option to select sex for MZ twin generation.
+Add option to tweak pedigree with one id provided
+
 # BGmisc 1.6.0.1
 * Add helper functions for checkParents etc
 * fixed incorrect direction so that parents are pointing to children in the graphs
diff --git a/R/buildComponent.R b/R/buildComponent.R
index 38a96522..9c86a8fa 100644
--- a/R/buildComponent.R
+++ b/R/buildComponent.R
@@ -19,6 +19,9 @@
 #' @param isChild_method character. The method to use for computing the isChild matrix.  Options are "classic" or "partialparent"
 #' @param adjBeta_method numeric The method to use for computing the building the adjacency_method matrix when using the "beta" build
 #' @param compress logical. If TRUE, use compression when saving the checkpoint files.  Defaults to TRUE.
+#' @param mz_twins logical. If TRUE, merge MZ co-twin columns in the r2 matrix before tcrossprod so that MZ twins are coded with relatedness 1 instead of 0.5. Twin pairs are identified from the \code{twinID} column. When a \code{zygosity} column is also present, only pairs where both members have \code{zygosity == "MZ"} are used; otherwise all \code{twinID} pairs are assumed to be MZ. Defaults to FALSE.
+#' @param mz_method character. The method to handle MZ twins.  Options are "merging" (default) or "addtwins".  "addtwins" adds the twin2 column to the twin1 column before tcrossprod so that all relatedness flows through a single source, then leaves the twin2 column as zero and relies on the fact that the row/col names are the same to copy the values back to twin2 after tcrossprod.  "merging" merges the twin2 column into the twin1 column before tcrossprod and then copies the values back to twin2 after tcrossprod so that both twins appear in the final matrix.
+#' @param beta logical. Used for benchmarking
 #' @param ... additional arguments to be passed to \code{\link{ped2com}}
 #' @details The algorithms and methodologies used in this function are further discussed and exemplified in the vignette titled "examplePedigreeFunctions". For more advanced scenarios and detailed explanations, consult this vignette.
 #' @export
@@ -32,7 +35,7 @@ ped2com <- function(ped, component,
                     standardize_colnames = TRUE,
                     transpose_method = "tcrossprod",
                     adjacency_method = "direct",
-                    isChild_method = "classic",
+                    isChild_method = "partialparent",
                     saveable = FALSE,
                     resume = FALSE,
                     save_rate = 5,
@@ -42,6 +45,9 @@ ped2com <- function(ped, component,
                     save_path = "checkpoint/",
                     adjBeta_method = NULL,
                     compress = TRUE,
+                    mz_twins = TRUE,
+                    mz_method = "addtwins",
+                    beta = FALSE,
                     ...) {
   #------
   # Check inputs
@@ -121,12 +127,37 @@ ped2com <- function(ped, component,
     ped <- standardizeColnames(ped, verbose = config$verbose)
   }
 
+  mz_row_pairs <- NULL
+  mz_id_pairs <- NULL
+
+  if (mz_twins == TRUE && "twinID" %in% colnames(ped)) {
+    df_mz <- findMZtwins(ped,
+      verbose = config$verbose,
+      returnIDs = TRUE,
+      returnRows = TRUE,
+      returnAsList = TRUE,
+      beta = beta
+    )
+    mz_row_pairs <- df_mz$pair_rows
+    mz_id_pairs <- df_mz$pair_ids
+  }
+
+
   # Load final result if computation was completed
   if (config$resume == TRUE && file.exists(checkpoint_files$final_matrix)) {
     if (config$verbose == TRUE) cat("Loading final computed matrix...\n")
     return(readRDS(checkpoint_files$final_matrix))
   }
 
+  if (mz_method %in% c("merging") && mz_twins == TRUE && !is.null(mz_row_pairs) && length(mz_row_pairs) > 0 &&
+    config$component %in% c("additive")) {
+    # replace all MZ twin IDs with the first twin's ID in each pair so they are merged for the path tracing and all subsequent steps.  We will copy the values back to the second twin at the end.
+    ped <- fuseTwins(ped = ped, mz_row_pairs = mz_row_pairs, mz_id_pairs = mz_id_pairs, config = config, beta = beta)
+    if (config$verbose == TRUE) {
+      message("Merged ", length(mz_row_pairs), " MZ twin pair(s) in pedigree dataset for path tracing")
+    }
+  }
+
 
   #------
   # Algorithm
@@ -141,6 +172,7 @@ ped2com <- function(ped, component,
     cat(paste0("Family Size = ", config$nr, "\n"))
   }
 
+  #
   # Step 1: Construct parent-child adjacency matrix
 
   ## A. Resume from Checkpoint if Needed
@@ -195,6 +227,8 @@ ped2com <- function(ped, component,
     config = config,
     compress = config$compress
   )
+
+  # TODO merge twin columns
   # --- Step 2: Compute Relatedness Matrix ---
 
 
@@ -226,7 +260,7 @@ ped2com <- function(ped, component,
   # r is I + A + A^2 + ... = (I-A)^-1 from RAM
   # could trim, here
   ## it keeps going until it explains all of the relatedness with themselves (i.e., mtSum == 0)
-  # some of this precision is articifuial because we literally get to the point that the condon is eaither there or not. probabiliticy
+  # some of this precision is artificial because we literally get to the point that the condon is eaither there or not. probabiliticy
 
   # how much percision do we need to get unbiased estimates
 
@@ -281,6 +315,29 @@ ped2com <- function(ped, component,
     compress = config$compress
   )
 
+  if (mz_method == "addtwins" && mz_twins == TRUE && !is.null(mz_row_pairs) && length(mz_row_pairs) > 0) {
+    if (config$verbose == TRUE) {
+      message("MZ twin merging enabled: Will merge MZ twin columns in r2 before tcrossprod")
+    }
+
+    # --- Step 3b: Add  ---
+    # MZ twins share the same genetic source.  We absorb twin2's column into
+    # twin1's before tcrossprod so all path-traced relatedness flows through a
+    # single source.  After tcrossprod we copy twin1's row/col back to twin2.
+    if (!is.null(mz_row_pairs) && length(mz_row_pairs) > 0 && config$component %in% c("additive")) {
+      # Extract all indices at once for batch operations
+      pairs_mat <- do.call(rbind, mz_row_pairs)
+      idx1_all <- pairs_mat[, 1]
+      idx2_all <- pairs_mat[, 2]
+      # Batch: absorb all twin2 columns into twin1 columns, then zero twin2
+      r2[, idx1_all] <- r2[, idx1_all, drop = FALSE] + r2[, idx2_all, drop = FALSE]
+      r2[, idx2_all] <- 0
+
+      if (config$verbose == TRUE) {
+        message("Added ", length(mz_row_pairs), " MZ twin pair column(s) in r2")
+      }
+    }
+  }
   # --- Step 4: T crossproduct  ---
 
   if (config$resume == TRUE && file.exists(checkpoint_files$tcrossprod_checkpoint) &&
@@ -300,12 +357,68 @@ ped2com <- function(ped, component,
     }
   }
 
+  if (mz_method %in% c("merging", "addtwins") && mz_twins == TRUE && config$component %in% c("additive") && !is.null(mz_row_pairs) && length(mz_row_pairs) > 0) {
+    # --- Step 4b: Restore MZ twins ---
+    # Copy twin1's row/col to twin2 so both twins appear in the final matrix.
+    if (config$sparse == FALSE) {
+      r <- as.matrix(r)
+      rnames <- rownames(r)
+      ids_mat <- do.call(rbind, mz_id_pairs)
+      idx1_all <- match(ids_mat[, 1], rnames)
+      idx2_all <- match(ids_mat[, 2], rnames)
+      # Batch copy: twin1 rows/cols -> twin2 rows/cols
+      r[idx2_all, ] <- r[idx1_all, ]
+
+      r[, idx2_all] <- r[, idx1_all]
+    } else {
+      # TODO this is really slow.  Can we do it without coercing to dense?  Maybe by doing row/col replacement on the sparse matrix directly?  Or by constructing a sparse matrix with the twin2 values and adding it to r?
+      #  r <- df_add
+
+      rnames <- r@Dimnames[[1]]
+
+      ids_mat <- do.call(rbind, mz_id_pairs)
+      # needs to use sparse indexing to avoid coercion to dense
+      idx1_all <- match(ids_mat[, 1], rnames)
+      idx2_all <- match(ids_mat[, 2], rnames)
+
+      twin1_rows <- r[idx1_all, , drop = FALSE]
+      twin1_cols <- r[, idx1_all, drop = FALSE]
+      twin1_rows@Dimnames[[1]] <- rnames[idx2_all]
+      twin1_cols@Dimnames[[2]] <- rnames[idx2_all]
+      twin1_self <- r[idx1_all, idx1_all, drop = FALSE]
+      twin1_self@Dimnames[[1]] <- rnames[idx2_all]
+
+      r[idx2_all, ] <- twin1_rows
+      r[, idx2_all] <- twin1_cols
+      r[idx2_all, idx2_all] <- twin1_self
+
+      # Batch copy: twin1 rows/cols -> twin2 rows/cols
+
+      # Row/column replacement on a dsCMatrix (symmetric) causes Matrix to
+      # coerce to dgCMatrix (general), doubling stored entries.  Convert back
+
+      r <- Matrix::drop0(r)
+
+      # so both mz_method paths return the same sparse class.
+      if (methods::is(r, "CsparseMatrix") && !methods::is(r, "symmetricMatrix")) {
+        r <- Matrix::forceSymmetric(r)
+      }
+    }
+    if (config$verbose == TRUE) {
+      message("Restored ", length(mz_row_pairs), " MZ twin pair(s) in relatedness matrix")
+    }
+  }
+
+
   if (config$component %in% c("mitochondrial", "mtdna", "mitochondria")) {
     r@x <- rep(1, length(r@x))
     # Assign 1 to all nonzero elements for mitochondrial component
   }
 
-  if (config$sparse == FALSE) {
+  # Remove explicit zeros so that both mz_method paths produce
+  # structurally identical sparse matrices
+
+  if (config$sparse == FALSE && !methods::is(r, "matrix")) {
     r <- as.matrix(r)
   }
   # flattens diagonal if you don't want to deal with inbreeding
@@ -335,6 +448,8 @@ ped2add <- function(ped, max_gen = 25, sparse = TRUE, verbose = FALSE,
                     save_rate_parlist = 100000 * save_rate,
                     save_path = "checkpoint/",
                     compress = TRUE,
+                    mz_twins = FALSE,
+                    mz_method = "addtwins",
                     ...) {
   ped2com(
     ped = ped,
@@ -353,6 +468,8 @@ ped2add <- function(ped, max_gen = 25, sparse = TRUE, verbose = FALSE,
     save_rate_parlist = save_rate_parlist,
     save_path = save_path,
     compress = compress,
+    mz_twins = mz_twins,
+    mz_method = mz_method,
     ...
   )
 }
diff --git a/R/constructAdjacency.R b/R/constructAdjacency.R
index 3053fb5b..fe3f7bbc 100644
--- a/R/constructAdjacency.R
+++ b/R/constructAdjacency.R
@@ -70,7 +70,8 @@
 .adjIndexed <- function(ped, component, saveable, resume,
                         save_path, verbose, lastComputed,
                         checkpoint_files, update_rate,
-                        parList, lens, save_rate_parlist, config, compress = config$compress) {
+                        parList, lens, save_rate_parlist,
+                        config, compress = config$compress) {
   # Loop through each individual in the pedigree
   # Build the adjacency matrix for parent-child relationships
   # Is person in column j the parent of the person in row i? .5 for yes, 0 for no.
@@ -207,7 +208,7 @@
   return(list_of_adjacency)
 }
 
-#' Construct Adjacency Matrix for Parent-Child Relationships Using Beta Method
+#' Construct Adjacency Matrix for Parent-Child Relationships Using Beta Methods
 #' This function constructs an adjacency matrix for parent-child relationships
 #' using a method in beta testing. It identifies parent-child pairs based on the
 #' specified component of relatedness.
diff --git a/R/helpTwins.R b/R/helpTwins.R
new file mode 100644
index 00000000..b8a2c33b
--- /dev/null
+++ b/R/helpTwins.R
@@ -0,0 +1,278 @@
+#' Determine isTwin Status
+#' @param ped pedigree data frame
+#' @return isTwin 'S' matrix
+#' @keywords internal
+
+
+isTwin <- function(ped) {
+  isTwin <- apply(ped[, c("twinID")], 1, function(x) {
+    !is.na(x)
+  })
+}
+
+
+#' Find MZ twin pair_rows in a pedigree
+#'
+#' Identifies MZ twin pair_rows from the \code{twinID} column and returns their
+#' row indices.  These indices are used later to merge the twins' columns in
+#' the \code{r2} matrix before \code{tcrossprod}, which correctly produces
+#' relatedness 1 between MZ co-twins with no diagonal or downstream artifacts.
+#'
+#' @param ped A pedigree data.frame with columns \code{ID} and \code{twinID}.
+#'   Optionally a \code{zygosity} column; when present only pair_rows where both
+#'   members have \code{zygosity == "MZ"} are used.
+#' @param verbose logical. If TRUE, print progress messages.
+#' @param returnIDs logical. If TRUE, return the IDs of the twin pair_rows instead of row indices.
+#' @param returnRows logical. If TRUE, return the row indices of the twin pair_rows instead of IDs.
+#' @param returnAsList logical. If TRUE, return results as a list of vectors
+#'  (default). If FALSE, return results as a data.frame with separate columns for each twin's ID and row index.
+#'  @param beta logical. If TRUE, use an optimized approach with O(1) lookups for large pedigrees. If FALSE (default), use a simpler approach that may be less efficient for large pedigrees.
+#' @return A list of length-2 integer vectors \code{c(idx1, idx2)} giving the
+#'   row indices of each MZ pair in the pedigree, or \code{NULL} if none found.
+#' @keywords internal
+findMZtwins <- function(ped, verbose = FALSE, returnRows = TRUE,
+                        returnIDs = FALSE, returnAsList = TRUE,
+                        beta = FALSE) {
+  if (!"twinID" %in% colnames(ped)) {
+    return(NULL)
+  }
+
+  twin_rows <- which(!is.na(ped$twinID))
+
+  # If zygosity column exists, restrict to MZ pair_rows
+  if ("zygosity" %in% colnames(ped)) {
+    twin_rows <- twin_rows[!is.na(ped$zygosity[twin_rows]) &
+      ped$zygosity[twin_rows] %in% c("mz", "MZ")]
+  }
+
+  if (length(twin_rows) == 0) {
+    return(NULL)
+  }
+
+  # Build ID-to-row lookup for O(1) resolution instead of which() per pair
+  id_to_row <- seq_len(nrow(ped))
+  names(id_to_row) <- as.character(ped$ID)
+
+  # Use environment as hash set for O(1) membership checks
+  processed <- new.env(hash = TRUE, parent = emptyenv())
+
+  pair_rows <- vector("list", length(twin_rows))
+  if (returnIDs) {
+    pair_ids <- vector("list", length(twin_rows))
+  }
+  n_pairs <- 0L
+
+  for (idx in twin_rows) {
+    twin_id <- ped$ID[idx]
+    co_twin_id <- ped$twinID[idx]
+
+    twin_id_chr <- as.character(twin_id)
+    co_twin_id_chr <- as.character(co_twin_id)
+
+    # Skip if already processed this pair (O(1) lookup)
+    if (exists(twin_id_chr, envir = processed, inherits = FALSE) ||
+      exists(co_twin_id_chr, envir = processed, inherits = FALSE)) {
+      next
+    }
+
+    # O(1) row lookup via named vector
+    idx1 <- id_to_row[twin_id_chr]
+    idx2 <- id_to_row[co_twin_id_chr]
+
+    if (is.na(idx1) || is.na(idx2)) next
+
+    # Always put the lower index first for consistency
+    if (idx1 > idx2) {
+      tmp <- idx1
+      idx1 <- idx2
+      idx2 <- tmp
+    }
+
+    # O(1) insert into hash set
+    assign(twin_id_chr, TRUE, envir = processed)
+    assign(co_twin_id_chr, TRUE, envir = processed)
+
+    n_pairs <- n_pairs + 1L
+    pair_rows[[n_pairs]] <- c(idx1, idx2)
+    if (returnIDs) {
+      pair_ids[[n_pairs]] <- c(twin_id, co_twin_id)
+    }
+    if (verbose) {
+      message(
+        "MZ twin pair found: ", twin_id, " (row ", idx1,
+        ") and ", co_twin_id, " (row ", idx2, ")"
+      )
+    }
+  }
+
+  # Trim pre-allocated lists to actual size
+  if (n_pairs == 0L) {
+    return(NULL)
+  }
+  pair_rows <- pair_rows[seq_len(n_pairs)]
+  if (returnIDs) {
+    pair_ids <- pair_ids[seq_len(n_pairs)]
+  }
+
+  if (returnIDs == TRUE && returnRows == FALSE) {
+    if (returnAsList == TRUE) {
+      return(pair_ids)
+    } else {
+      data.frame(
+        twin1_id = vapply(pair_ids, `[`, numeric(1), 1L),
+        twin2_id = vapply(pair_ids, `[`, numeric(1), 2L)
+      )
+    }
+  } else if (returnRows == TRUE && returnIDs == FALSE) {
+    if (returnAsList == TRUE) {
+      return(pair_rows)
+    } else {
+      data.frame(
+        twin1_row = vapply(pair_rows, `[`, integer(1), 1L),
+        twin2_row = vapply(pair_rows, `[`, integer(1), 2L)
+      )
+    }
+  } else if (returnIDs == TRUE && returnRows == TRUE) {
+    if (returnAsList == TRUE) {
+      return(list(pair_rows = pair_rows, pair_ids = pair_ids))
+    } else {
+      return(data.frame(
+        twin1_id = vapply(pair_ids, `[`, numeric(1), 1L),
+        twin2_id = vapply(pair_ids, `[`, numeric(1), 2L),
+        twin1_row = vapply(pair_rows, `[`, integer(1), 1L),
+        twin2_row = vapply(pair_rows, `[`, integer(1), 2L)
+      ))
+    }
+  } else {
+    stop("Invalid combination of returnRows and returnIDs parameters")
+  }
+}
+# replace all MZ twin IDs with the first twin's ID in each pair so they are merged for the path tracing and all subsequent steps.  We will copy the values back to the second twin at the end.
+
+#' Fuse MZ twin pairs in a pedigree dataset for path tracing
+#' This function identifies MZ twin pairs in the pedigree dataset and merges their IDs for path tracing purposes. The second twin in each pair is made a founder (with NA parents), and all children of the second twin are redirected to the first twin. This allows for correct relatedness calculations without diagonal or downstream artifacts.
+#' @param ped A pedigree data.frame with columns \code{ID}, \code{momID}, \code{dadID}, and optionally \code{twinID} and \code{zygosity}. The function will look for MZ twin pairs based on the \code{twinID} column and optionally restrict to MZ pairs if a \code{zygosity} column is present.
+#' @param mz_id_pairs Optional list of length-2 character vectors specifying the IDs of MZ twin pairs to fuse. If provided, this will be used instead of automatically identifying MZ twins from the \code{twinID} column. Each element should be a character vector of length 2, e.g. \code{list(c("ID1", "ID2"), c("ID3", "ID4"))}.
+#' @param mz_row_pairs Optional list of length-2 integer vectors specifying the row indices of MZ twin pairs to fuse. If provided, this will be used instead of automatically identifying MZ twins from the \code{twinID} column. Each element should be an integer vector of length 2, e.g. \code{list(c(1, 2), c(3, 4))}.
+#' @param test_df_twins logical. If TRUE, return the data frame of twin pairs instead of the modified pedigree. Default is FALSE.
+#' @param df_twins Optional data frame with columns \code{twin1_id}, \code{twin2_id}, \code{twin1_row}, and \code{twin2_row} specifying the IDs and row indices of MZ twin pairs to fuse. If provided, this will be used instead of automatically identifying MZ twins from the \code{twinID} column. If this parameter is provided, it takes precedence over \code{mz_id_pairs} and \code{mz_row_pairs}. If \code{test_df_twins} is TRUE, this data frame will be returned for testing purposes instead of performing the fusion.
+#' @param beta logical. If TRUE, use an optimized approach with O(1) lookups for large pedigrees when identifying MZ twins. Default is FALSE.
+#' @param config A list of configuration options.
+#' @return A modified version of the input pedigree data.frame with MZ twin pairs fused for path tracing. If \code{test_df_twins} is TRUE, returns the data frame of identified twin pairs instead.
+# 70% of the time is here
+fuseTwins <- function(ped,
+                      df_twins = NULL,
+                      mz_id_pairs = NULL,
+                      mz_row_pairs = NULL,
+                      config = list(verbose = FALSE),
+                      test_df_twins = FALSE,
+                      beta = FALSE) {
+  # make df_twins if not already made, and test it if requested, before proceeding with the fusion.  This allows users to provide their own mz_id_pairs or mz_row_pairs and have them converted to df_twins for testing before the fusion is attempted.
+
+  if (!is.null(df_twins)) {
+    if (!all(c("twin1_id", "twin2_id", "twin1_row", "twin2_row") %in% colnames(df_twins))) {
+      stop("df_twins must have columns twin1_id, twin2_id, twin1_row, and twin2_row")
+    }
+    if (test_df_twins == TRUE) {
+      return(df_twins)
+    }
+  } else if (!is.null(mz_id_pairs) && !is.null(mz_row_pairs) && length(mz_id_pairs) == length(mz_row_pairs)) {
+    df_twins <- lapply(1:length(mz_id_pairs), function(i) {
+      twin1_id <- mz_id_pairs[[i]][1]
+      twin2_id <- mz_id_pairs[[i]][2]
+      twin1_row <- mz_row_pairs[[i]][1]
+      twin2_row <- mz_row_pairs[[i]][2]
+      data.frame(
+        twin1_id = twin1_id, twin2_id = twin2_id,
+        twin1_row = twin1_row, twin2_row = twin2_row
+      )
+    })
+    df_twins <- do.call(rbind, df_twins)
+    rownames(df_twins) <- NULL
+    if (test_df_twins == TRUE) {
+      return(df_twins)
+    }
+  } else if (is.null(mz_id_pairs) && is.null(mz_row_pairs)) {
+    df_twins <- findMZtwins(ped,
+      verbose = config$verbose,
+      returnRows = TRUE, returnIDs = TRUE, returnAsList = FALSE
+    )
+    if (test_df_twins == TRUE) {
+      return(df_twins)
+    }
+  } else if (!is.null(mz_id_pairs) && is.null(mz_row_pairs)) {
+    df_twins <- lapply(mz_id_pairs, function(pair) {
+      twin1_row <- which(ped$ID == pair[1])
+      twin2_row <- which(ped$ID == pair[2])
+      data.frame(
+        twin1_id = pair[1], twin2_id = pair[2],
+        twin1_row = twin1_row, twin2_row = twin2_row
+      )
+    })
+    df_twins <- do.call(rbind, df_twins)
+    rownames(df_twins) <- NULL
+    if (test_df_twins == TRUE) {
+      return(df_twins)
+    }
+  } else if (is.null(mz_id_pairs) && !is.null(mz_row_pairs)) {
+    df_twins <- lapply(mz_row_pairs, function(row) {
+      twin1_id <- ped$ID[row[1]]
+      twin2_id <- ped$ID[row[2]]
+      data.frame(
+        twin1_id = twin1_id,
+        twin2_id = twin2_id,
+        twin1_row = row[1],
+        twin2_row = row[2]
+      )
+    })
+    df_twins <- do.call(rbind, df_twins)
+    # remove row names
+    rownames(df_twins) <- NULL
+    if (test_df_twins == TRUE) {
+      return(df_twins)
+    }
+  } else {
+    stop("Invalid input: must provide either mz_id_pairs, mz_row_pairs, or df_twins")
+  }
+
+
+  fuseattemptable <- !is.null(df_twins) || (!is.null(mz_id_pairs) && length(mz_id_pairs) > 0) || (!is.null(mz_row_pairs) && length(mz_row_pairs) > 0)
+  if (fuseattemptable == TRUE) {
+    if (config$verbose == TRUE) {
+      message("MZ twin pairs identified for fusion")
+    }
+
+    twin1s_id <- df_twins$twin1_id
+    twin2s_id <- df_twins$twin2_id
+    twin2s_row <- df_twins$twin2_row
+
+    # Make twin2s founders
+    #    ped$momID[twin2s_row] <- NA
+    #   ped$dadID[twin2s_row] <- NA
+
+    twin2s_as_mom <- which(ped$momID %in% twin2s_id)
+    twin2s_as_dad <- which(ped$dadID %in% twin2s_id)
+    # Now redirect all children of twin2 to twin1
+    ped$momID[twin2s_as_mom] <- twin1s_id[match(ped$momID[twin2s_as_mom], twin2s_id)]
+    ped$dadID[twin2s_as_dad] <- twin1s_id[match(ped$dadID[twin2s_as_dad], twin2s_id)]
+
+    if ("spouseID" %in% colnames(ped)) {
+      twin2s_as_spouse <- which(ped$spouseID %in% twin2s_id)
+      ped$spouseID[twin2s_as_spouse] <- twin1s_id[match(ped$spouseID[twin2s_as_spouse], twin2s_id)]
+    }
+    if ("spID" %in% colnames(ped)) {
+      twin2s_as_spID <- which(ped$spID %in% twin2s_id)
+      ped$spID[twin2s_as_spID] <- twin1s_id[match(ped$spID[twin2s_as_spID], twin2s_id)]
+    }
+
+    if (config$verbose == TRUE) {
+      message("Merged ", length(twin1s_id), " MZ twin pair(s) in pedigree dataset for path tracing")
+    }
+  } else {
+    if (config$verbose == TRUE) {
+      message("No MZ twin pair_rows found in pedigree dataset")
+    }
+  }
+
+  return(ped)
+}
diff --git a/R/segmentPedigree.R b/R/segmentPedigree.R
index 3179dbfd..d74abfb3 100644
--- a/R/segmentPedigree.R
+++ b/R/segmentPedigree.R
@@ -10,6 +10,7 @@
 #' @param momID character.  Name of the column in ped for the mother ID variable
 #' @param dadID character.  Name of the column in ped for the father ID variable
 #' @param famID character.  Name of the column to be created in ped for the family ID variable
+#' @param twinID character.  Name of the column in ped for the twin ID variable, if applicable
 #' @param ... additional arguments to be passed to \code{\link{ped2com}}
 #' @details
 #' The general idea of this function is to use person ID, mother ID, and father ID to
@@ -29,17 +30,25 @@
 #'
 ped2fam <- function(ped, personID = "ID",
                     momID = "momID", dadID = "dadID", famID = "famID",
+                    twinID = "twinID",
                     ...) {
   # Call to wrapper function
-  .ped2id(ped = ped, personID = personID, momID = momID, dadID = dadID, famID = famID, type = "parents")
+  .ped2id(
+    ped = ped, personID = personID, momID = momID, dadID = dadID, famID = famID, twinID = twinID,
+    type = "parents"
+  )
 }
 
 .ped2id <- function(ped,
                     personID = "ID", momID = "momID", dadID = "dadID",
-                    famID = "famID", type,
+                    famID = "famID", twinID = "twinID",
+                    type,
                     ...) {
   # Turn pedigree into family
-  pg <- ped2graph(ped = ped, personID = personID, momID = momID, dadID = dadID, adjacent = type)
+  pg <- ped2graph(
+    ped = ped, personID = personID, momID = momID, dadID = dadID, twinID = twinID,
+    adjacent = type
+  )
 
   # Find weakly connected components of graph
   wcc <- igraph::components(pg)
@@ -97,6 +106,7 @@ ped2graph <- function(ped,
                       personID = "ID",
                       momID = "momID",
                       dadID = "dadID",
+                      twinID = "twinID",
                       directed = TRUE,
                       adjacent = c("parents", "mothers", "fathers"),
                       ...) {
@@ -196,11 +206,14 @@ ped2graph <- function(ped,
 #'
 ped2maternal <- function(ped, personID = "ID",
                          momID = "momID", dadID = "dadID",
-                         matID = "matID", ...) {
+                         matID = "matID",
+                         twinID = "twinID",
+                         ...) {
   # Call to wrapper function
   .ped2id(
     ped = ped, personID = personID, momID = momID,
-    dadID = dadID, famID = matID, type = "mothers"
+    dadID = dadID, famID = matID, twinID = twinID,
+    type = "mothers"
   )
 }
 
@@ -219,10 +232,14 @@ ped2maternal <- function(ped, personID = "ID",
 #'
 ped2paternal <- function(ped, personID = "ID",
                          momID = "momID", dadID = "dadID",
-                         patID = "patID", ...) {
+                         patID = "patID",
+                         twinID = "twinID",
+                         ...) {
   # Call to wrapper function
   .ped2id(
     ped = ped, personID = personID, momID = momID,
-    dadID = dadID, famID = patID, type = "fathers"
+    dadID = dadID, famID = patID,
+    twinID = twinID,
+    type = "fathers"
   )
 }
diff --git a/R/tweakPedigree.R b/R/tweakPedigree.R
index 88f96128..1c8d4e6a 100644
--- a/R/tweakPedigree.R
+++ b/R/tweakPedigree.R
@@ -9,6 +9,7 @@
 #' @param verbose logical.  If TRUE, print progress through stages of algorithm
 #' @param gen_twin A vector of \code{generation} of the twin to be imputed.
 #' @param zygosity A character string indicating the zygosity of the twins. Default is "MZ" for monozygotic twins.
+#' @param twin_sex A character string indicating the sex of the twins. Default is randomly assigned ("R"). If specified, it should be either "M" or "F"
 #' @return Returns a \code{data.frame} with MZ twins information added as a new column.
 #' @export
 
@@ -18,7 +19,8 @@ makeTwins <- function(ped, ID_twin1 = NA_integer_,
                       ID_twin2 = NA_integer_,
                       gen_twin = 2,
                       verbose = FALSE,
-                      zygosity = "MZ") {
+                      zygosity = "MZ",
+                      twin_sex = "R") {
   # Check if the ped is the same format as the output of simulatePedigree
   if (paste0(colnames(ped), collapse = "") != paste0(c(
     "famID", "ID", "gen",
@@ -30,37 +32,57 @@ makeTwins <- function(ped, ID_twin1 = NA_integer_,
     }
     # stop("The input pedigree is not in the same format as the output of simulatePedigree")
   }
+
   ped$MZtwin <- NA_integer_
-  ped$zygosity <- NA_character_
+  ped$MZzygosity <- NA_character_
   # Check if the two IDs are provided
-  if (is.na(ID_twin1) || is.na(ID_twin2)) {
+
+  # Build ID-to-row index for O(1) attribute lookups
+  id_row_map <- seq_len(nrow(ped))
+  names(id_row_map) <- as.character(ped$ID)
+
+  if (is.na(ID_twin1) && is.na(ID_twin2)) {
     # Check if the generation is provided
     if (is.na(gen_twin)) {
       stop("You should provide either the IDs of the twins or the generation of the twins")
     } else {
       # Check if the generation is valid
       if (gen_twin < 2 || gen_twin > max(ped$gen)) {
-        stop("The generation of the twins should be an integer between 2 and the maximum generation in the pedigree")
+        warning("The generation of the twins should be an integer between 2 and the maximum generation in the pedigree")
+        # remove the MZtwin and zygosity columns
+        ped$MZzygosity <- NULL
+        ped$MZtwin <- NULL
+        return(ped)
       } else {
         idx <- nrow(ped[ped$gen == gen_twin & !is.na(ped$dadID), ])
         usedID <- c()
         # randomly loop through all the individuals in the generation until find an individual who is the same sex and shares the same dadID and momID with another individual
+        # Pre-compute generation mask once to avoid repeated subsetting
+        gen_mask <- ped$gen == gen_twin & !is.na(ped$dadID)
+        gen_ids <- ped$ID[gen_mask]
+        # Build ID-to-row index for O(1) attribute lookups
+        id_row_map <- seq_len(nrow(ped))
+        names(id_row_map) <- as.character(ped$ID)
         for (i in 1:idx) {
-          # cat("loop", i, "\n")
           # check if i is equal to the number of individuals in the generation
           usedID <- c(usedID, ID_twin1)
           # message(usedID)
           if (i < idx) {
             # randomly select one individual from the generation
-            ID_twin1 <- resample(ped$ID[ped$gen == gen_twin & !(ped$ID %in% usedID) & !is.na(ped$dadID)], 1)
-            # cat("twin1", ID_twin1, "\n")
+            ID_twin1 <- resample(ped$ID[gen_mask & !(ped$ID %in% usedID)], 1)
+            # Cache twin1 attributes via O(1) row lookup
+            twin1_row <- id_row_map[as.character(ID_twin1)]
+            twin1_sex <- ped$sex[twin1_row]
+            twin1_dad <- ped$dadID[twin1_row]
+            twin1_mom <- ped$momID[twin1_row]
             # find one same sex sibling who has the same dadID and momID as the selected individual
-            if (zygosity %in% c("MZ", "SS")) {
-              twin2_Pool <- ped$ID[ped$ID != ID_twin1 & ped$gen == gen_twin & ped$sex == ped$sex[ped$ID == ID_twin1] & ped$dadID == ped$dadID[ped$ID == ID_twin1] & ped$momID == ped$momID[ped$ID == ID_twin1]]
-            } else if (zygosity == "DZ") {
-              twin2_Pool <- ped$ID[ped$ID != ID_twin1 & ped$gen == gen_twin & ped$dadID == ped$dadID[ped$ID == ID_twin1] & ped$momID == ped$momID[ped$ID == ID_twin1]]
-            } else if (zygosity == "OS") {
-              twin2_Pool <- ped$ID[ped$ID != ID_twin1 & ped$gen == gen_twin & ped$sex != ped$sex[ped$ID == ID_twin1] & ped$dadID == ped$dadID[ped$ID == ID_twin1] & ped$momID == ped$momID[ped$ID == ID_twin1]]
+            sib_mask <- ped$ID != ID_twin1 & gen_mask & ped$dadID == twin1_dad & ped$momID == twin1_mom
+            if (zygosity %in% c("mz", "MZ", "SS", "ss")) {
+              twin2_Pool <- ped$ID[sib_mask & ped$sex == twin1_sex]
+            } else if (zygosity %in% c("DZ", "dz")) {
+              twin2_Pool <- ped$ID[sib_mask]
+            } else if (zygosity %in% c("OS", "os")) {
+              twin2_Pool <- ped$ID[sib_mask & ped$sex != twin1_sex]
             } else {
               stop("The zygosity should be either 'MZ', 'DZ', or 'OS'")
             }
@@ -81,7 +103,14 @@ makeTwins <- function(ped, ID_twin1 = NA_integer_,
           } else {
             # randomly select all males or females in the generation and put them in a vector
             if (zygosity %in% c("MZ", "SS")) {
-              selectGender <- ped$ID[ped$gen == gen_twin & ped$sex == resample(c("M", "F"), 1) & !is.na(ped$dadID) & !is.na(ped$momID)]
+              if (twin_sex == "R") {
+                twin_sex_select <- resample(c("M", "F"), 1)
+              } else {
+                twin_sex_select <- twin_sex
+              }
+
+              selectGender <- ped$ID[ped$gen == gen_twin & ped$sex == twin_sex_select & !is.na(ped$dadID) & !is.na(ped$momID)]
+              notselectGender <- ped$ID[ped$gen == gen_twin & ped$sex != twin_sex_select & !is.na(ped$dadID) & !is.na(ped$momID)]
             } else if (zygosity %in% c("DZ")) {
               selectGender <- ped$ID[ped$gen == gen_twin & !is.na(ped$dadID) & !is.na(ped$momID)]
             } else if (zygosity %in% c("OS")) {
@@ -91,9 +120,13 @@ makeTwins <- function(ped, ID_twin1 = NA_integer_,
             }
 
             # message(selectGender)
-            if (length(selectGender) < 2) {
+            if (length(selectGender) < 2 && length(notselectGender) < 2) {
               stop("There are no available same-sex people in the generation to make twins")
+            } else if (twin_sex == "R" && length(selectGender) < 2 && length(notselectGender) >= 2) {
+              selectGender <- notselectGender
             }
+
+
             # randomly select two individuals from the vector
             ID_DoubleTwin <- resample(selectGender, 2)
             # message(ID_DoubleTwin)
@@ -112,18 +145,84 @@ makeTwins <- function(ped, ID_twin1 = NA_integer_,
         # Set the zygosity of the twins
       }
     }
-  } else {
+  } else if (!is.na(ID_twin1) && !is.na(ID_twin2)) {
     # Impute the IDs of the twin in the MZtwin column
     ped$MZtwin[ped$ID == ID_twin1] <- ID_twin2
     ped$MZtwin[ped$ID == ID_twin2] <- ID_twin1
+  } else if (!is.na(ID_twin1) && is.na(ID_twin2)) {
+
+    twin1_row <- id_row_map[as.character(ID_twin1)]
+    twin1_sex <- ped$sex[twin1_row]
+    twin1_dad <- ped$dadID[twin1_row]
+    twin1_mom <- ped$momID[twin1_row]
+    sib_mask <- ped$ID != ID_twin1 & !is.na(ped$dadID) & ped$dadID == twin1_dad & !is.na(ped$momID) & ped$momID == twin1_mom
+    if (zygosity %in% c("mz", "MZ", "SS", "ss")) {
+      twin2_Pool <- ped$ID[sib_mask & ped$sex == twin1_sex]
+    } else if (zygosity %in% c("DZ", "dz")) {
+      twin2_Pool <- ped$ID[sib_mask]
+    } else if (zygosity %in% c("OS", "os")) {
+      twin2_Pool <- ped$ID[sib_mask & ped$sex != twin1_sex]
+    } else {
+      stop("The zygosity should be either 'MZ', 'DZ', or 'OS'")
+    }
+    twin2_Pool <- twin2_Pool[!is.na(twin2_Pool)]
+    if (length(twin2_Pool) == 0) {
+      stop("No suitable sibling found for ID_twin1 = ", ID_twin1, " with zygosity '", zygosity, "'")
+    }
+    ID_twin2 <- resample(twin2_Pool, 1)
+    if (verbose) cat("Auto-selected ID_twin2 =", ID_twin2, "\n")
+  } else if (is.na(ID_twin1) && !is.na(ID_twin2)) {
+    # Mirror: find a match for twin2
+    twin2_row <- id_row_map[as.character(ID_twin2)]
+    twin2_sex <- ped$sex[twin2_row]
+    twin2_dad <- ped$dadID[twin2_row]
+    twin2_mom <- ped$momID[twin2_row]
+    sib_mask <- ped$ID != ID_twin2 & !is.na(ped$dadID) & ped$dadID == twin2_dad & !is.na(ped$momID) & ped$momID == twin2_mom
+    if (zygosity %in% c("mz", "MZ", "SS", "ss")) {
+      twin1_Pool <- ped$ID[sib_mask & ped$sex == twin2_sex]
+    } else if (zygosity %in% c("DZ", "dz")) {
+      twin1_Pool <- ped$ID[sib_mask]
+    } else if (zygosity %in% c("OS", "os")) {
+      twin1_Pool <- ped$ID[sib_mask & ped$sex != twin2_sex]
+    } else {
+      stop("The zygosity should be either 'MZ', 'DZ', or 'OS'")
+    }
+
+
+    twin1_Pool <- twin1_Pool[!is.na(twin1_Pool)]
+    if (length(twin1_Pool) == 0) {
+      stop("No suitable sibling found for ID_twin2 = ", ID_twin2, " with zygosity '", zygosity, "'")
+    }
+    ID_twin1 <- resample(twin1_Pool, 1)
+    if (verbose) cat("Auto-selected ID_twin1 =", ID_twin1, "\n")
   }
+
+
   if (verbose == TRUE) {
     cat("twin1", ID_twin1, "\n")
     cat("twin2", ID_twin2, "\n")
   }
-  names(ped)[names(ped) == "MZtwin"] <- "twinID"
-  ped$zygosity[ped$ID == ID_twin1] <- zygosity
-  ped$zygosity[ped$ID == ID_twin2] <- zygosity
+  ped$MZtwin[ped$ID == ID_twin1] <- ID_twin2
+  ped$MZtwin[ped$ID == ID_twin2] <- ID_twin1
+
+  if ("twinID" %in% colnames(ped)) {
+
+    ped$twinID[!is.na(ped$MZtwin)] <- ped$MZtwin[!is.na(ped$MZtwin)]
+    ped$MZtwin <- NULL
+  } else {
+    names(ped)[names(ped) == "MZtwin"] <- "twinID"
+
+  }
+  if ("zygosity" %in% colnames(ped)) {
+    ped$zygosity[ped$ID == ID_twin1] <- zygosity
+    ped$zygosity[ped$ID == ID_twin2] <- zygosity
+    ped$MZzygosity <- NULL
+  } else {
+    names(ped)[names(ped) == "MZzygosity"] <- "zygosity"
+    ped$zygosity[ped$ID == ID_twin1] <- zygosity
+    ped$zygosity[ped$ID == ID_twin2] <- zygosity
+  }
+
   return(ped)
 }
 
@@ -139,6 +238,7 @@ makeTwins <- function(ped, ID_twin1 = NA_integer_,
 #' @param verbose logical.  If TRUE, print progress through stages of algorithm
 #' @param gen_inbred A vector of \code{generation} of the twin to be imputed.
 #' @param type_inbred A character vector indicating the type of inbreeding. "sib" for sibling inbreeding and "cousin" for cousin inbreeding.
+#' @param prefer_unmated A logical indicating whether to prefer unmated siblings when automatically selecting inbred mates. Default is FALSE, which means the function will consider all siblings regardless of their mating status.
 #' @return Returns a \code{data.frame} with some inbred mates.
 #' @details
 #' This function creates inbred mates in the simulated pedigree \code{data.frame}. This function's purpose is to evaluate the effect of inbreeding on model fitting and parameter estimation. In case it needs to be said, we do not condone inbreeding in real life. But we recognize that it is a common practice in some fields to create inbred strains for research purposes.
@@ -150,7 +250,9 @@ makeInbreeding <- function(ped,
                            ID_mate2 = NA_integer_,
                            verbose = FALSE,
                            gen_inbred = 2,
-                           type_inbred = "sib") {
+                           type_inbred = "sib",
+                           prefer_unmated = FALSE
+) {
   # check if the ped is the same format as the output of simulatePedigree
 
   if (paste0(colnames(ped),
@@ -175,8 +277,55 @@ makeInbreeding <- function(ped,
     stop("The type of inbreeding should be either 'sib' or 'cousin'")
     return()
   }
-  # check if the two IDs are provided
-  if (is.na(ID_mate1) || is.na(ID_mate2)) {
+
+  if (!is.na(ID_mate1) && is.na(ID_mate2)) {
+    mate1_sex <- ped$sex[ped$ID == ID_mate1]
+    mate1_dad <- ped$dadID[ped$ID == ID_mate1]
+    mate1_mom <- ped$momID[ped$ID == ID_mate1]
+    if (!is.na(mate1_dad) && !is.na(mate1_mom)) {
+      # prefer unmated opposite-sex sibling
+      if (prefer_unmated == TRUE) {
+        pool <- makePool(ped = ped, mate_id = ID_mate1, mate_sex = mate1_sex,
+          mate_dad = mate1_dad, mate_mom = mate1_mom, prefer_unmated = TRUE)
+
+        if (length(pool) == 0) {
+          # fall back to mated opposite-sex sibling
+          pool <-  makePool(ped = ped, mate_id = ID_mate1, mate_sex = mate1_sex,
+            mate_dad = mate1_dad, mate_mom = mate1_mom, prefer_unmated = FALSE)
+        }
+      } else {
+        pool <- makePool(ped = ped, mate_id = ID_mate1, mate_sex = mate1_sex,
+          mate_dad = mate1_dad, mate_mom = mate1_mom, prefer_unmated = FALSE)
+      }
+
+      if (length(pool) > 0) {
+        ID_mate2 <- resample(pool, 1)
+        if (verbose) message("Auto-selected ID_mate2 = ", ID_mate2)
+      }
+    }
+  } else if (is.na(ID_mate1) && !is.na(ID_mate2)) {
+    mate2_sex <- ped$sex[ped$ID == ID_mate2]
+    mate2_dad <- ped$dadID[ped$ID == ID_mate2]
+    mate2_mom <- ped$momID[ped$ID == ID_mate2]
+    if (!is.na(mate2_dad) && !is.na(mate2_mom)) {
+      if (prefer_unmated) {
+        pool <- makePool(ped = ped, mate_id = ID_mate2, mate_sex = mate2_sex,
+          mate_dad = mate2_dad, mate_mom = mate2_mom, prefer_unmated = TRUE)
+        # fall back
+        if (length(pool) == 0) {
+          pool <- makePool(ped = ped, mate_id = ID_mate2, mate_sex = mate2_sex,
+            mate_dad = mate2_dad, mate_mom = mate2_mom, prefer_unmated = FALSE)
+        }
+      } else {
+        pool <- makePool(ped = ped, mate_id = ID_mate2, mate_sex = mate2_sex,
+          mate_dad = mate2_dad, mate_mom = mate2_mom, prefer_unmated = FALSE)
+      }
+      if (length(pool) > 0) {
+        ID_mate1 <- resample(pool, 1)
+        if (verbose) message("Auto-selected ID_mate1 = ", ID_mate1)
+      }
+    }  # check if the two IDs are provided
+  } else  if (is.na(ID_mate1) && is.na(ID_mate2)) {
     # Check if the generation is provided
     if (is.na(gen_inbred)) {
       stop("You should provide either the IDs of the inbred mates or the generation of the inbred mates")
@@ -195,15 +344,35 @@ makeInbreeding <- function(ped,
             if (!is.na(ID_mate2)) {
               break
             }
-            ID_pool_mate1 <- ped$ID[ped$gen == gen_inbred & !is.na(ped$dadID) & !is.na(ped$momID) & is.na(ped$spID) & !(ped$ID %in% usedID)]
-            # if the pool is empty, find all individuals who have the same dadID and momID as the selected individual but mated
-            if (length(ID_pool_mate1) == 0) {
+
+            if (prefer_unmated) {
+              # try to find one opposite
+              ID_pool_mate1 <- ped$ID[ped$gen == gen_inbred & !is.na(ped$dadID) & !is.na(ped$momID) & is.na(ped$spID) & !(ped$ID %in% usedID)]
+
+
+
+              # if the pool is empty, find all individuals who have the same dadID and momID as the selected individual but mated
+              if (length(ID_pool_mate1) == 0) {
+                ID_pool_mate1 <- ped$ID[ped$gen == gen_inbred & !is.na(ped$dadID) & !is.na(ped$momID) & !(ped$ID %in% usedID)]
+              }
+            } else {
               ID_pool_mate1 <- ped$ID[ped$gen == gen_inbred & !is.na(ped$dadID) & !is.na(ped$momID) & !(ped$ID %in% usedID)]
             }
+
             ID_mate1 <- resample(ID_pool_mate1, 1)
             usedID <- c(usedID, ID_mate1)
             # try to find one opposite-sex individual who has the same dadID and momID as the selected individual, preferalbly not mated
-            ID_pool_mate2 <- ped$ID[ped$gen == gen_inbred & ped$sex != ped$sex[ped$ID == ID_mate1] & ped$dadID == ped$dadID[ped$ID == ID_mate1] & ped$momID == ped$momID[ped$ID == ID_mate1] & is.na(ped$spID)]
+            if (prefer_unmated) {
+              ID_pool_mate2 <- ped$ID[ped$gen == gen_inbred & ped$sex != ped$sex[ped$ID == ID_mate1] & ped$dadID == ped$dadID[ped$ID == ID_mate1] & ped$momID == ped$momID[ped$ID == ID_mate1] & is.na(ped$spID)]
+              # back up
+              if (length(ID_pool_mate1) == 0) {
+                ID_pool_mate2 <- ped$ID[ped$gen == gen_inbred & ped$sex != ped$sex[ped$ID == ID_mate1] & ped$dadID == ped$dadID[ped$ID == ID_mate1] & ped$momID == ped$momID[ped$ID == ID_mate1]]
+              }
+            } else {
+              #     ID_pool_mate2 <- ped$ID[ped$gen == gen_inbred & ped$sex != ped$sex[ped$ID == ID_mate1] & ped$dadID == ped$dadID[ped$ID == ID_mate1] & ped$momID == ped$momID[ped$ID == ID_mate1]]
+              ID_pool_mate2 <- makePool(ped = ped, mate_id = ID_mate1, mate_sex = ped$sex[ped$ID == ID_mate1], mate_dad = ped$dadID[ped$ID == ID_mate1], mate_mom = ped$momID[ped$ID == ID_mate1], prefer_unmated = prefer_unmated, gen_inbred = gen_inbred)
+            }
+
             # if the pool is not empty, randomly select one individual from the pool
             if (length(ID_pool_mate2) > 0) {
               ID_mate2 <- resample(ID_pool_mate2, 1)
@@ -237,20 +406,18 @@ makeInbreeding <- function(ped,
   # change the spouseID of ID_mate1 and ID_mate2 to each other
   ped$spID[ped$ID == ID_mate1] <- ID_mate2
   ped$spID[ped$ID == ID_mate2] <- ID_mate1
-  # change the individuals in next generation whose dadID and momID are ID_mate1 and ID_mate2's former mates to ID_mate1 and ID_mate2
-  for (j in seq_len(nrow(ped))) {
-    if (!is.na(ped$dadID[j]) & !is.na(ID_mate1_former_mate) & ped$dadID[j] == ID_mate1_former_mate) {
-      ped$dadID[j] <- ID_mate2
-    }
-    if (!is.na(ped$momID[j]) & !is.na(ID_mate1_former_mate) & ped$momID[j] == ID_mate1_former_mate) {
-      ped$momID[j] <- ID_mate2
-    }
-    if (!is.na(ped$dadID[j]) & !is.na(ID_mate2_former_mate) & ped$dadID[j] == ID_mate2_former_mate) {
-      ped$dadID[j] <- ID_mate1
-    }
-    if (!is.na(ped$momID[j]) & !is.na(ID_mate2_former_mate) & ped$momID[j] == ID_mate2_former_mate) {
-      ped$momID[j] <- ID_mate1
-    }
+  # Vectorized replacement of former mates' IDs in children's parent columns
+  if (!is.na(ID_mate1_former_mate)) {
+    dad_match1 <- which(!is.na(ped$dadID) & ped$dadID == ID_mate1_former_mate)
+    if (length(dad_match1) > 0) ped$dadID[dad_match1] <- ID_mate2
+    mom_match1 <- which(!is.na(ped$momID) & ped$momID == ID_mate1_former_mate)
+    if (length(mom_match1) > 0) ped$momID[mom_match1] <- ID_mate2
+  }
+  if (!is.na(ID_mate2_former_mate)) {
+    dad_match2 <- which(!is.na(ped$dadID) & ped$dadID == ID_mate2_former_mate)
+    if (length(dad_match2) > 0) ped$dadID[dad_match2] <- ID_mate1
+    mom_match2 <- which(!is.na(ped$momID) & ped$momID == ID_mate2_former_mate)
+    if (length(mom_match2) > 0) ped$momID[mom_match2] <- ID_mate1
   }
   return(ped)
 }
@@ -265,13 +432,28 @@ makeInbreeding <- function(ped,
 #' @param gen_drop the generation in which the randomly dropped person is. Will work if `ID_drop` is not specified.
 #' @param sex_drop the biological sex of the randomly dropped person.
 #' @param n_drop the number of times the mutation happens.
+#' @param verbose logical.  If TRUE, print progress through stages of algorithm
 #' @return a pedigree with the dropped person's `dadID` and `momID` set to NA.
 #' @export
 dropLink <- function(ped,
                      ID_drop = NA_integer_,
                      gen_drop = 2,
                      sex_drop = NA_character_,
-                     n_drop = 1) {
+                     n_drop = 1,
+                     verbose = FALSE) {
+  # Standardize column names for consistency with other functions
+
+  if (paste0(colnames(ped),
+    collapse = ""
+  ) != paste0(
+    c("famID", "ID", "gen", "dadID", "momID", "spID", "sex"),
+    collapse = ""
+  )) {
+    ped <- standardizeColnames(ped, verbose = verbose)
+    if (verbose == TRUE) {
+      message("The input pedigree is not in the same format as the output of simulatePedigree\n")
+    }
+  }
   # check if the ID_drop is specified
   if (is.na(ID_drop)) {
     # check if the sex_drop is specified
@@ -406,3 +588,29 @@ addPersonToPed <- function(ped, name = NULL,
     return(rbind(ped, new_row))
   }
 }
+
+
+makePool <- function(ped, mate_id, mate_sex, mate_dad, mate_mom, prefer_unmated = FALSE,
+                     gen_inbred  = NULL, usedID = NULL
+) {
+  if (!is.null(gen_inbred)) {
+    ped <- ped[ped$gen == gen_inbred, ]
+  }
+  if (!is.null(usedID)) {
+    ped <- ped[!(ped$ID %in% usedID), ]
+  }
+
+
+  # should we prefer unmated siblings when automatically selecting inbred mates? If yes, we will only consider unmated siblings. If no, we will consider all siblings regardless of their mating status.
+  if (prefer_unmated == TRUE) {
+    mated_mask <- !is.na(ped$spID)
+
+  } else {
+    mated_mask <- rep(TRUE, nrow(ped))
+  }
+
+  pool <- ped$ID[ped$ID != mate_id & ped$sex != mate_sex &
+    !is.na(ped$dadID) & ped$dadID == mate_dad &
+    !is.na(ped$momID) & ped$momID == mate_mom & mated_mask]
+  return(pool)
+}
diff --git a/data-raw/optimizing.R b/data-raw/optimizing.R
index c279bf79..1a11914f 100644
--- a/data-raw/optimizing.R
+++ b/data-raw/optimizing.R
@@ -3,19 +3,11 @@ library(microbenchmark)
 library(tidyverse)
 set.seed(1164127)
 Ngen <- 3
-kpc <- 6
+kpc <- 4
 sexR <- .50 # sometimes fails above .5
-marR <- .9
+marR <- .8
 reps <- 15
-if (FALSE) {
-  profvis({
-    simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = beta_F)
-  })
 
-  profvis({
-    simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = beta_T)
-  })
-}
 # mz_method_opts <- c("addtwins", "merging")
 beta_method_opts <- c(TRUE, FALSE)
 beta_F <- T
@@ -29,114 +21,189 @@ df_gen1 <- simulatePedigree(
 ) %>%
   makeTwins(gen_twin = 1)
 
-df_lowgen <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = F) %>%
+df_lowgen <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE) %>%
+  makeTwins(gen_twin = gen_twin)
+
+df_midgen <- simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = TRUE) %>%
+  makeTwins(gen_twin = gen_twin)
+
+df_highgen <- simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = TRUE) %>%
+  makeTwins(gen_twin = gen_twin)
+
+# r_mz1 <- df_midgen %>%
+# ped2com(mz_method = "merging", mz_twins = TRUE)
+# r_mz2 <- df_midgen %>%
+#  ped2com(mz_method = "addtwins", mz_twins = TRUE)
+# expect_equal(length(r_mz1@i), length(r_mz2@i))
+# expect_equal(length(r_mz1@x), length(r_mz2@x))
+# expect_equal(length(r_mz1@p), length(r_mz2@p))
+
+df_gen1 <- simulatePedigree(
+  kpc = kpc, Ngen = 1, sexR = sexR, marR = marR,
+  beta = TRUE
+) %>%
+  makeTwins(gen_twin = 1)
+
+df_lowgen <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = T) %>%
   makeTwins(gen_twin = gen_twin)
 
-df_midgen <- simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = F) %>%
+df_midgen <- simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = T) %>%
   makeTwins(gen_twin = gen_twin)
 
-df_highgen <- simulatePedigree(kpc = kpc, Ngen = Ngen * 2 + 1, sexR = sexR, marR = marR, beta = F) %>%
+df_highgen <- simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = T) %>%
   makeTwins(gen_twin = gen_twin)
 
-r_mz1 <- df_midgen %>%
-  ped2add(mz_method = "merging", mz_twins = TRUE)
-r_mz2 <- df_midgen %>%
-  ped2add(mz_method = "addtwins", mz_twins = TRUE)
+#r_mz1 <- df_midgen %>%
+#  ped2com(mz_method = "merging", mz_twins = TRUE)
+#r_mz2 <- df_midgen %>%
+#  ped2com(mz_method = "addtwins", mz_twins = TRUE)
 # expect_equal(length(r_mz1@i), length(r_mz2@i))
 # expect_equal(length(r_mz1@x), length(r_mz2@x))
 # expect_equal(length(r_mz1@p), length(r_mz2@p))
 
 benchmark_results <- microbenchmark(
-  beta_null_1gen = {
+  null_1gen = {
     df_gen1 %>%
-      ped2add(mz_twins = F)
+      ped2com(mz_twins = F,component = "additive")
   },
-  beta_false_1gen = {
+
+  addtwins_1gen = {
     df_gen1 %>%
-      ped2add(mz_method = "addtwins", mz_twins = TRUE)
+      ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive")
   },
-  beta_true_1gen = {
+  merging_1gen = {
     df_gen1 %>%
-      ped2add(mz_method = "merging", mz_twins = TRUE)
+      ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive")
   },
-  beta_null_lowgen = {
+  null_beta_1gen = {
+    df_gen1 %>%
+      ped2com(mz_twins = F,component = "additive", beta = TRUE)
+  },
+  addtwins_beta_1gen = {
+    df_gen1 %>%
+      ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive",beta=TRUE)
+  },
+  merging_beta_1gen = {
+    df_gen1 %>%
+      ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive",beta=TRUE)
+  },
+  null_lowgen = {
+    df_lowgen %>%
+      ped2com(mz_twins = F,component = "additive")
+  },
+  addtwins_lowgen = {
+    df_lowgen %>%
+      ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive")
+  },
+  merging_lowgen = {
+    df_lowgen %>%
+      ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive")
+  },
+  null_beta_lowgen = {
     df_lowgen %>%
-      ped2add(mz_twins = F)
+      ped2com(mz_twins = F,component = "additive", beta=TRUE)
   },
-  beta_false_lowgen = {
+  addtwins_beta_lowgen = {
     df_lowgen %>%
-      ped2add(mz_method = "addtwins", mz_twins = TRUE)
+      ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive", beta=TRUE)
   },
-  beta_true_lowgen = {
+  merging_beta_lowgen = {
     df_lowgen %>%
-      ped2add(mz_method = "merging", mz_twins = TRUE)
+      ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive", beta=TRUE)
   },
-  beta_null_midgen = {
+  null_midgen = {
     df_midgen %>%
-      ped2add(mz_twins = F)
+      ped2com(mz_twins = F,component = "additive")
   },
-  beta_false_midgen = {
+  addtwins_midgen = {
     df_midgen %>%
-      ped2add(mz_method = "addtwins", mz_twins = TRUE)
+      ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive")
   },
-  beta_true_midgen = {
+  merging_midgen = {
     df_midgen %>%
-      ped2add(mz_method = "merging", mz_twins = TRUE)
+      ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive")
   },
-  beta_null_highgen = {
-    df_highgen %>%
-      ped2add(mz_twins = F)
+  null_beta_midgen = {
+    df_midgen %>%
+      ped2com(mz_twins = F,component = "additive", beta=TRUE)
   },
-  beta_false_highgen = {
-    df_highgen %>%
-      ped2add(mz_method = "addtwins", mz_twins = TRUE)
+  addtwins_beta_midgen = {
+    df_midgen %>%
+      ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive", beta=TRUE)
   },
-  beta_true_highgen = {
-    df_highgen %>%
-      ped2add(mz_method = "merging", mz_twins = TRUE)
+  merging_beta_midgen = {
+    df_midgen %>%
+      ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive", beta=TRUE)
   },
+  # null_highgen = {
+  #   df_highgen %>%
+  #     ped2com(mz_twins = F,component = "additive")
+  # },
+  # addtwins_highgen = {
+  #   df_highgen %>%
+  #     ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive")
+  # },
+  # merging_highgen = {
+  #   df_highgen %>%
+  #     ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive")
+  # },
+  # null_beta_highgen = {
+  #   df_highgen %>%
+  #     ped2com(mz_twins = F,component = "additive", beta=TRUE)
+  # },
+  # addtwins_beta_highgen = {
+  #   df_highgen %>%
+  #     ped2com(mz_method = "addtwins", mz_twins = TRUE,component = "additive", beta=TRUE)
+  # },
+  # merging_beta_highgen = {
+  #   df_highgen %>%
+  #     ped2com(mz_method = "merging", mz_twins = TRUE,component = "additive", beta=TRUE)
+  # },
   times = reps # Run each method 10 times
 )
 
 
-
-
 benchmark_results <- benchmark_results %>%
   mutate(
-    beta_factor = factor(case_when(
-      grepl("beta_true", expr) ~ "TRUE",
-      grepl("beta_false", expr) ~ "FALSE",
-      grepl("beta_null", expr) ~ "NULL",
-      grepl("beta_indexed", expr) ~ "indexed"
+    twin_factor = factor(case_when(
+      grepl("merging", expr) ~ "merging",
+      grepl("addtwins", expr) ~ "addtwins",
+      grepl("null", expr) ~ "NULL"
     )),
-    beta = ifelse(grepl("beta_false", expr), FALSE, TRUE),
+    twin_factor = factor(twin_factor, levels = c("NULL", "addtwins", "merging")),
+    beta = ifelse(grepl("beta_", expr), TRUE,FALSE),
     gen_num = case_when(
       grepl("1gen", expr) ~ 1,
       grepl("lowgen", expr) ~ Ngen,
       grepl("midgen", expr) ~ Ngen * 2,
-      grepl("highgen", expr) ~ Ngen * 2 + 1
+      grepl("highgen", expr) ~ Ngen * 3
     ),
     gen_factor = factor(gen_num, levels = c(1, Ngen, Ngen * 2, Ngen * 2 + 1))
   )
 
 summary(benchmark_results)
-lm(benchmark_results$time ~ benchmark_results$beta_factor * benchmark_results$gen_num) %>%
+lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$twin_factor * benchmark_results$gen_num) %>%
+  summary()
+lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$gen_num) %>%
   summary()
 
-lm(benchmark_results$time ~ benchmark_results$beta_factor) %>%
+lm(benchmark_results$time ~ benchmark_results$twin_factor * benchmark_results$beta) %>%
   summary()
 # log transform time for better visualization
 
-ggplot(benchmark_results, aes(x = gen_factor, y = time / 1e6, color = beta_factor)) +
-  geom_boxplot() +
+ggplot(benchmark_results, aes(x = gen_factor, y = time / 1e6, color = twin_factor, fill = beta)) +
+  geom_boxplot(notch = TRUE, position = position_dodge(width = 0.8)) +
   labs(
     title = "Benchmarking simulatePedigree() with and without beta parameter",
     x = "Generation Size",
     y = "Execution Time (ms)",
-    color = "Beta Parameter"
+    color = "Twin Method"
   ) +
   theme_minimal() +
-  scale_y_log10()
+  scale_y_log10() +
+  scale_fill_manual(values = c("TRUE" = "black", "FALSE" = "white"), name = "Beta Parameter") +
+  scale_color_manual(values = c("NULL" = "gray", "addtwins" = "skyblue3",
+  "merging" = "tomato2"), name = "Twin Method")
 
 
 
@@ -149,6 +216,7 @@ kpc <- 4
 sexR <- .50 # sometimes fails above .5
 marR <- .7
 reps <- 10
+
 if (FALSE) {
   profvis({
     simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = FALSE)
@@ -158,60 +226,93 @@ if (FALSE) {
     simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE)
   })
 }
-if (FALSE) {
-  benchmark_results <- microbenchmark(
-    beta_false_1gen = {
-      simulatePedigree(kpc = kpc, Ngen = 1, sexR = sexR, marR = marR, beta = FALSE)
-    },
-    beta_true_1gen = {
-      simulatePedigree(kpc = kpc, Ngen = 1, sexR = sexR, marR = marR, beta = TRUE)
-    },
-    beta_false_lowgen = {
-      simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = FALSE)
-    },
-    beta_true_lowgen = {
-      simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE)
-    },
-    beta_false_midgen = {
-      simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = FALSE)
-    },
-    beta_true_midgen = {
-      simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = TRUE)
-    },
-    beta_false_highgen = {
-      simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = FALSE)
-    },
-    beta_true_highgen = {
-      simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = TRUE)
-    },
-    times = reps # Run each method 10 times
-  )
-
-  benchmark_results <- benchmark_results %>%
-    mutate(
-      beta_factor = factor(case_when(
-        grepl("beta_true", expr) ~ "TRUE",
-        grepl("beta_false", expr) ~ "FALSE",
-        grepl("beta_indexed", expr) ~ "indexed"
-      )),
-      beta = ifelse(grepl("beta_false", expr), FALSE, TRUE),
-      gen_num = case_when(
-        grepl("1gen", expr) ~ 1,
-        grepl("lowgen", expr) ~ Ngen,
-        grepl("midgen", expr) ~ Ngen * 2,
-        grepl("highgen", expr) ~ Ngen * 3
-      ),
-      gen_factor = factor(gen_num, levels = c(1, Ngen, Ngen * 2, Ngen * 3))
+  if (FALSE) {
+    benchmark_results <- microbenchmark(
+      beta_false_1gen = {
+        simulatePedigree(kpc = kpc, Ngen = 1, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_1gen = {
+        simulatePedigree(kpc = kpc, Ngen = 1, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      beta_false_lowgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_lowgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      beta_false_midgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_midgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      beta_false_highgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_highgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      times = reps # Run each method 10 times
     )
 
-  summary(benchmark_results)
-  lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$gen_num) %>%
-    summary()
+    benchmark_results <- benchmark_results %>%
+      mutate(
+        beta_factor = factor(case_when(
+          grepl("beta_true", expr) ~ "TRUE",
+          grepl("beta_false", expr) ~ "FALSE",
+          grepl("beta_indexed", expr) ~ "indexed"
+        )),
+        beta = ifelse(grepl("beta_false", expr), FALSE, TRUE),
+        gen_num = case_when(
+          grepl("1gen", expr) ~ 1,
+          grepl("lowgen", expr) ~ Ngen,
+          grepl("midgen", expr) ~ Ngen * 2,
+          grepl("highgen", expr) ~ Ngen * 3
+        ),
+        gen_factor = factor(gen_num, levels = c(1, Ngen, Ngen * 2, Ngen * 3))
+      )
+    benchmark_results <- benchmark_results %>%
+      mutate(
+        beta_factor = factor(case_when(
+          grepl("beta_true", expr) ~ "TRUE",
+          grepl("beta_false", expr) ~ "FALSE",
+          grepl("beta_indexed", expr) ~ "indexed"
+        )),
+        beta = ifelse(grepl("beta_false", expr), FALSE, TRUE),
+        gen_num = case_when(
+          grepl("1gen", expr) ~ 1,
+          grepl("lowgen", expr) ~ Ngen,
+          grepl("midgen", expr) ~ Ngen * 2,
+          grepl("highgen", expr) ~ Ngen * 3
+        ),
+        gen_factor = factor(gen_num, levels = c(1, Ngen, Ngen * 2, Ngen * 3))
+      )
+
+    summary(benchmark_results)
+    lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$gen_num) %>%
+      summary()
+    summary(benchmark_results)
+    lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$gen_num) %>%
+      summary()
 
-  lm(benchmark_results$time ~ benchmark_results$beta) %>%
-    summary()
-  # log transform time for better visualization
+    lm(benchmark_results$time ~ benchmark_results$beta) %>%
+      summary()
+    # log transform time for better visualization
+    lm(benchmark_results$time ~ benchmark_results$beta) %>%
+      summary()
+    # log transform time for better visualization
 
+    ggplot(benchmark_results, aes(x = gen_factor, y = time / 1e6, color = beta_factor)) +
+      geom_boxplot() +
+      labs(
+        title = "Benchmarking simulatePedigree() with and without beta parameter",
+        x = "Generation Size",
+        y = "Execution Time (ms)",
+        color = "Beta Parameter"
+      ) +
+      theme_minimal() +
+      scale_y_log10()
+  }
   ggplot(benchmark_results, aes(x = gen_factor, y = time / 1e6, color = beta_factor)) +
     geom_boxplot() +
     labs(
diff --git a/data-raw/optimizing_simulations.R b/data-raw/optimizing_simulations.R
new file mode 100644
index 00000000..e824760d
--- /dev/null
+++ b/data-raw/optimizing_simulations.R
@@ -0,0 +1,117 @@
+library(profvis)
+library(microbenchmark)
+library(tidyverse)
+set.seed(1667)
+Ngen <- 3
+kpc <- 4
+sexR <- .50 # sometimes fails above .5
+marR <- .7
+reps <- 10
+
+if (FALSE) {
+  profvis({
+    simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = FALSE)
+  })
+
+  profvis({
+    simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE)
+  })
+}
+  if (FALSE) {
+    benchmark_results <- microbenchmark(
+      beta_false_1gen = {
+        simulatePedigree(kpc = kpc, Ngen = 1, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_1gen = {
+        simulatePedigree(kpc = kpc, Ngen = 1, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      beta_false_lowgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_lowgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      beta_false_midgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_midgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 2, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      beta_false_highgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = FALSE)
+      },
+      beta_true_highgen = {
+        simulatePedigree(kpc = kpc, Ngen = Ngen * 3, sexR = sexR, marR = marR, beta = TRUE)
+      },
+      times = reps # Run each method 10 times
+    )
+
+    benchmark_results <- benchmark_results %>%
+      mutate(
+        beta_factor = factor(case_when(
+          grepl("beta_true", expr) ~ "TRUE",
+          grepl("beta_false", expr) ~ "FALSE",
+          grepl("beta_indexed", expr) ~ "indexed"
+        )),
+        beta = ifelse(grepl("beta_false", expr), FALSE, TRUE),
+        gen_num = case_when(
+          grepl("1gen", expr) ~ 1,
+          grepl("lowgen", expr) ~ Ngen,
+          grepl("midgen", expr) ~ Ngen * 2,
+          grepl("highgen", expr) ~ Ngen * 3
+        ),
+        gen_factor = factor(gen_num, levels = c(1, Ngen, Ngen * 2, Ngen * 3))
+      )
+    benchmark_results <- benchmark_results %>%
+      mutate(
+        beta_factor = factor(case_when(
+          grepl("beta_true", expr) ~ "TRUE",
+          grepl("beta_false", expr) ~ "FALSE",
+          grepl("beta_indexed", expr) ~ "indexed"
+        )),
+        beta = ifelse(grepl("beta_false", expr), FALSE, TRUE),
+        gen_num = case_when(
+          grepl("1gen", expr) ~ 1,
+          grepl("lowgen", expr) ~ Ngen,
+          grepl("midgen", expr) ~ Ngen * 2,
+          grepl("highgen", expr) ~ Ngen * 3
+        ),
+        gen_factor = factor(gen_num, levels = c(1, Ngen, Ngen * 2, Ngen * 3))
+      )
+
+    summary(benchmark_results)
+    lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$gen_num) %>%
+      summary()
+    summary(benchmark_results)
+    lm(benchmark_results$time ~ benchmark_results$beta * benchmark_results$gen_num) %>%
+      summary()
+
+    lm(benchmark_results$time ~ benchmark_results$beta) %>%
+      summary()
+    # log transform time for better visualization
+    lm(benchmark_results$time ~ benchmark_results$beta) %>%
+      summary()
+    # log transform time for better visualization
+
+    ggplot(benchmark_results, aes(x = gen_factor, y = time / 1e6, color = beta_factor)) +
+      geom_boxplot() +
+      labs(
+        title = "Benchmarking simulatePedigree() with and without beta parameter",
+        x = "Generation Size",
+        y = "Execution Time (ms)",
+        color = "Beta Parameter"
+      ) +
+      theme_minimal() +
+      scale_y_log10()
+  }
+  ggplot(benchmark_results, aes(x = gen_factor, y = time / 1e6, color = beta_factor)) +
+    geom_boxplot() +
+    labs(
+      title = "Benchmarking simulatePedigree() with and without beta parameter",
+      x = "Generation Size",
+      y = "Execution Time (ms)",
+      color = "Beta Parameter"
+    ) +
+    theme_minimal() +
+    scale_y_log10()
+}
diff --git a/data-raw/optimizing_twins.R b/data-raw/optimizing_twins.R
new file mode 100644
index 00000000..511f3207
--- /dev/null
+++ b/data-raw/optimizing_twins.R
@@ -0,0 +1,379 @@
+library(profvis)
+library(microbenchmark)
+library(tidyverse)
+devtools::load_all(".")
+
+
+# ---------------------------
+# 0) Config
+# ---------------------------
+cfg <- list(
+  seed = 1164127,
+  Ngen_base = 3,
+  reps = 10,
+  all_scenarios = FALSE, # set to TRUE to run all scenarios defined below
+  include_highgen = TRUE,
+  include_1gen = FALSE,
+  include_lowgen = FALSE
+)
+cfg$gen_twin <- ceiling(cfg$Ngen_base - 1)
+
+set.seed(cfg$seed)
+
+# ---------------------------
+# 1) Levels (edit here to extend)
+# ---------------------------
+levels <- list(
+  ped = tibble(
+    ped_label = c(
+      if (cfg$include_1gen) "1gen",
+      if (cfg$include_lowgen) "lowgen",
+      "midgen",
+      if (cfg$include_highgen) "highgen"
+    ),
+    Ngen_total = c(
+      if (cfg$include_1gen) 1,
+      if (cfg$include_lowgen) cfg$Ngen_base,
+      cfg$Ngen_base * 2,
+      if (cfg$include_highgen) cfg$Ngen_base * 3
+    ),
+    gen_twin = c(
+      if (cfg$include_1gen) 1,
+      if (cfg$include_lowgen) cfg$gen_twin,
+      cfg$gen_twin,
+      if (cfg$include_highgen) cfg$gen_twin
+    )
+    # Add highgen row whenever you want
+  ),
+
+  # Simulation-side factors (simulatePedigree)
+  kpc = 3, # set to c(2, 3, 4) to vary
+  sexR = 0.50, # sometimes fails above .5
+  marR = c(0.8), # set to c(0.6, 0.8, 0.9) to vary
+  sim_beta = TRUE, # set to c(TRUE, FALSE) if you ever want to vary
+
+  # Conversion-side factors (ped2com)
+  component = c("additive"),
+  twin_method = c("NULL", "addtwins", "merging"),
+  beta = c(FALSE, TRUE),
+  sparse_matrix = c(FALSE, TRUE) # user-facing name, translated to ped2com's `sparse`
+)
+
+# Which columns define a unique simulation vs conversion condition
+# If you add a new factor, put its name in the right scope here.
+
+scopes <- list(
+  sim  = c("ped_label", "Ngen_total", "gen_twin", "kpc", "sexR", "marR", "sim_beta"),
+  conv = c("component", "twin_method", "beta", "sparse_matrix")
+)
+
+# ---------------------------
+# 2) Scenarios (edit here to control crossing)
+# ---------------------------
+# Each scenario says what to vary and what to fix. No other code changes.
+scenarios <- list(
+  full = list(
+    vary  = c("ped", "marR", "twin_method", "beta", "sparse_matrix"),
+    fixed = list()
+  ),
+  quick = list(
+    vary  = c("ped", "twin_method"),
+    fixed = list(marR = 0.8, beta = FALSE, sparse_matrix = TRUE, component = "additive")
+  )
+
+  # Add more scenarios whenever you want:
+  # e.g., "marR_only" = list(vary=c("marR"), fixed=list(ped = levels$ped[levels$ped$ped_label=="midgen",], ...))
+)
+
+if (!cfg$all_scenarios) {
+  scenarios <- scenarios[c("full")] # order control; also, comment out any you don't want to run
+}
+# ---------------------------
+# 3) Generic design builder (do not edit to add factors)
+# ---------------------------
+`%||%` <- function(x, y) if (!is.null(x)) x else y
+
+level_tbl <- function(name, x) {
+  if (inherits(x, "data.frame")) {
+    return(x)
+  }
+  tibble(!!name := x)
+}
+
+default_value <- function(x) {
+  if (inherits(x, "data.frame")) {
+    return(x[1, , drop = FALSE])
+  }
+  x[[1]]
+}
+
+expand_scenario <- function(levels, vary, fixed = list(), scenario = "scenario") {
+  df <- tibble(.dummy = 1)
+
+  # expand varied factors
+  for (nm in vary) {
+    df <- tidyr::crossing(df, level_tbl(nm, levels[[nm]]))
+  }
+
+  # add fixed/default factors not varied
+  not_varied <- setdiff(names(levels), vary)
+  for (nm in not_varied) {
+    lv <- levels[[nm]]
+
+    if (inherits(lv, "data.frame")) {
+      const <- fixed[[nm]] %||% default_value(lv)
+      if (!inherits(const, "data.frame") || nrow(const) != 1) const <- default_value(lv)
+      df <- bind_cols(df, const[rep(1, nrow(df)), , drop = FALSE])
+    } else {
+      df[[nm]] <- fixed[[nm]] %||% default_value(lv)
+    }
+  }
+
+  df %>%
+    select(-.dummy) %>%
+    mutate(scenario = scenario, .before = 1)
+}
+
+build_design <- function(levels, scenarios) {
+  purrr::imap_dfr(
+    scenarios,
+    ~ expand_scenario(levels, vary = .x$vary, fixed = .x$fixed, scenario = .y)
+  )
+}
+
+design <- build_design(levels, scenarios)
+
+# Create a stable, joinable label (also becomes the microbenchmark expr name)
+design <- design %>%
+  mutate(
+    label = paste0(
+      scenario,
+      "|ped=", ped_label,
+      "|marR=", marR,
+      "|twin=", twin_method,
+      "|beta=", beta,
+      "|sparse=", sparse_matrix,
+      "|comp=", component
+    )
+  )
+
+# ---------------------------
+# 4) Simulation cache (simulate once per unique sim condition)
+# ---------------------------
+simulate_one <- function(Ngen_total, gen_twin, kpc, sexR, marR, sim_beta) {
+  simulatePedigree(
+    kpc = kpc, Ngen = Ngen_total, sexR = sexR, marR = marR,
+    beta = sim_beta
+  ) %>%
+    makeTwins(gen_twin = gen_twin)
+}
+
+sim_tbl <- design %>%
+  distinct(across(all_of(scopes$sim))) %>%
+  mutate(
+    sim_id = row_number(),
+    ped = pmap(
+      list(Ngen_total, gen_twin, kpc, sexR, marR, sim_beta, sim_id),
+      function(Ngen_total, gen_twin, kpc, sexR, marR, sim_beta, sim_id) {
+        set.seed(cfg$seed + sim_id)
+        simulate_one(Ngen_total, gen_twin, kpc, sexR, marR, sim_beta)
+      }
+    )
+  ) %>%
+  select(-sim_id)
+
+design <- design %>% left_join(sim_tbl, by = scopes$sim)
+
+# Put peds and args in keyed lists so benchmark expressions stay tiny
+peds_by_label <- setNames(design$ped, design$label)
+
+# ---------------------------
+# 5) Conversion arg translation (edit only if you add non-1:1 args)
+# ---------------------------
+# Columns that map directly to ped2com arg names go through automatically.
+# Anything else gets a translation rule here.
+special_to_args <- list(
+  twin_method = function(v) {
+    if (is.null(v) || length(v) == 0 || is.na(v) || v == "NULL") {
+      list(mz_twins = FALSE)
+    } else {
+      list(mz_twins = TRUE, mz_method = v)
+    }
+  },
+  beta = function(v) list(beta = TRUE),
+  sparse_matrix = function(v) list(sparse = v)
+)
+
+make_conv_args <- function(row, conv_cols) {
+  # row is already a named list of scalar values when called correctly
+  direct_cols <- setdiff(conv_cols, names(special_to_args))
+  args <- row[direct_cols]
+
+  for (nm in intersect(names(special_to_args), conv_cols)) {
+    args <- c(args, special_to_args[[nm]](row[[nm]]))
+  }
+
+  args
+}
+
+make_conv_args_row <- function(...) {
+  row <- list(...)
+  make_conv_args(row, scopes$conv)
+}
+
+# Correct per-row args creation
+args_by_label <- design %>%
+  mutate(
+    conv_args = pmap(select(., all_of(scopes$conv)), make_conv_args_row)
+  ) %>%
+  select(label, conv_args) %>%
+  deframe()
+
+# ---------------------------
+# 6) One microbenchmark call with all expressions (correct behavior)
+# ---------------------------
+bench_exprs <- lapply(names(peds_by_label), function(lbl) {
+  bquote(
+    do.call(ped2com, c(list(ped = peds_by_label[[.(lbl)]]), args_by_label[[.(lbl)]]))
+  )
+})
+names(bench_exprs) <- names(peds_by_label)
+
+write_csv(design, "data-raw/ped2com_benchmark_design.csv")
+# write start time
+write.table(Sys.time(), "data-raw/ped2com_benchmark_start_time.txt", row.names = FALSE, col.names = FALSE)
+
+
+benchmark_results <- do.call(
+  microbenchmark::microbenchmark,
+  c(bench_exprs, list(times = cfg$reps))
+)
+
+write.table(Sys.time(), "data-raw/ped2com_benchmark_end_time.txt", row.names = FALSE, col.names = FALSE)
+
+results <- as_tibble(benchmark_results) %>%
+  mutate(label = as.character(expr)) %>%
+  left_join(
+    design %>% select(-ped),
+    by = "label"
+  )
+# notes: sparse with addtwins is slow (878 vs 250), but sparse with merging is way times slower (14257 vs 241). This is a huge difference, and suggests that the merging method is not compatible with sparse matrices in its current form. The addtwins method is slower than NULL in both cases, but the difference is much more pronounced when using sparse matrices. The means of adding or substituting twins in the pedigree may interact with the way sparse matrices are constructed or handled in ped2com, leading to increased computational overhead. The NULL method is the fastest in both cases, which is expected since it does not involve any additional processing for twins. Overall, these results suggest that while the addtwins method can be used with sparse matrices, it may not be the most efficient choice, and the merging method may not be suitable for use with sparse matrices at all.
+# ---------------------------
+# 7) Analysis/plot
+# ---------------------------
+results <- results %>%
+  mutate(
+    twin_method = factor(twin_method, levels = c("NULL", "addtwins", "merging")),
+    ped_label = factor(ped_label, levels = levels$ped$ped_label),
+    gen_factor = factor(ped_label, levels = levels$ped$ped_label, labels = paste0(levels$ped$Ngen_total, " gen"))
+  )
+write_csv(results, "data-raw/ped2com_benchmark_results.csv")
+
+summary(results)
+results %>%
+  group_by(ped_label, twin_method, sparse_matrix) %>%
+  summarise(median_time_ms = median(time / 1e6),
+    mean_time_ms = mean(time / 1e6),
+    var_time_ms = var(time / 1e6),
+    sd_time_ms = sd(time / 1e6),
+    se_time_ms = sd(time / 1e6) / sqrt(n()),
+    n_time = n(),
+    .groups = "drop_last") %>%
+  arrange(ped_label, twin_method, sparse_matrix) %>%
+  write_csv("data-raw/ped2com_benchmark_summary.csv")
+
+# ped_label twin_method sparse_matrix median_time_ms
+#<fct>     <fct>       <lgl>                  <dbl>
+# 7 highgen   NULL        FALSE                 222.
+# 8 highgen   NULL        TRUE                  198.
+# 9 highgen   addtwins    FALSE                 250.
+# 10 highgen   addtwins    TRUE                  878.
+# 11 highgen   merging     FALSE                 241.
+# 12 highgen   merging     TRUE                14257.
+
+if (cfg$reps > 8) {
+  notch <- FALSE
+} else {
+  notch <- FALSE
+}
+
+results %>%
+  #  dplyr::filter(!ped_label %in% c("1gen", "lowgen", "midgen")) %>%
+  mutate(
+    beta_sparse = paste0("beta=", beta, ", sparse=", sparse_matrix),
+    beta_sparse = factor(beta_sparse, levels = c(
+      "beta=FALSE, sparse=FALSE",
+      "beta=FALSE, sparse=TRUE",
+      "beta=TRUE, sparse=FALSE",
+      "beta=TRUE, sparse=TRUE"
+    ))
+  ) %>%
+  ggplot(
+    aes(
+      x = ped_label,
+      y = time / 1e6,
+      fill = twin_method,
+      color = sparse_matrix # ,
+      #  shape = beta
+    )
+  ) +
+  geom_boxplot(
+    notch = notch, position = position_dodge(width = 0.8), outlier.size = 0.5,
+    linewidth = 0.25
+  ) +
+  scale_y_log10() +
+  facet_grid(~scenario) +
+  labs(
+    title = "Benchmarking ped2com() by twin handling and beta option",
+    x = "Pedigree", y = "Execution time (ms)", color = "sparse_matrix", fill = "Twin method"
+  ) +
+  theme_minimal() #+
+#  scale_fill_manual(values = c(
+#    "beta=FALSE, sparse=FALSE" = "lightgray",
+#    "beta=FALSE, sparse=TRUE" = "gray8",
+#    "beta=TRUE, sparse=FALSE" = "lightcoral",
+#    "beta=TRUE, sparse=TRUE" = "red2"
+#  )) +
+#  scale_color_manual(values = c("NULL" = "gray", "addtwins" = "skyblue3", "merging" = "tomato2"))
+# df <- sim_tbl$ped[[2]]
+
+# df_add <- df %>% ped2add()
+
+if (FALSE) {
+  profvis(
+    {
+      sim_tbl$ped[[1]] %>% ped2com(component = "additive", mz_twins = TRUE, mz_method = "addtwins", beta = TRUE, sparse = TRUE)
+    },
+    interval = 0.01)
+
+
+  profvis(
+    {
+      sim_tbl$ped[[1]] %>% ped2com(component = "additive", mz_twins = TRUE, mz_method = "merging", beta = TRUE, sparse = FALSE)
+    },
+    interval = 0.01)
+  # could it be the interaction between NA and gc??
+  test_df_twins <-  sim_tbl$ped[[1]] %>% fuseTwins(test_df_twins = TRUE)
+  foundreturnedall <-  findMZtwins(sim_tbl$ped[[1]],
+    verbose = F,
+    returnRows = TRUE, returnIDs = TRUE, returnAsList = T
+  )
+  foundreturnedrows <-  findMZtwins(sim_tbl$ped[[1]],
+    verbose = F,
+    returnRows = TRUE, returnIDs = FALSE, returnAsList = T
+  )
+  foundreturnedids <-  findMZtwins(sim_tbl$ped[[1]],
+    verbose = F,
+    returnRows = FALSE, returnIDs = TRUE, returnAsList = T
+  )
+
+}
+if (FALSE) {
+  # Example of how to run a single benchmark for one condition
+  profvis(
+    {
+      sim_tbl$ped[[1]] %>% fuseTwins(mz_id_pairs = NULL, mz_row_pairs = NULL)
+    },
+    interval = 0.01
+  )
+}
diff --git a/data-raw/ped2com_benchmark_design.csv b/data-raw/ped2com_benchmark_design.csv
new file mode 100644
index 00000000..593138ce
--- /dev/null
+++ b/data-raw/ped2com_benchmark_design.csv
@@ -0,0 +1,25 @@
+scenario,ped_label,Ngen_total,gen_twin,marR,twin_method,beta,sparse_matrix,kpc,sexR,sim_beta,component,label,ped
+full,highgen,9,2,0.8,addtwins,FALSE,FALSE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=FALSE|comp=additive,
+full,highgen,9,2,0.8,addtwins,FALSE,TRUE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=TRUE|comp=additive,
+full,highgen,9,2,0.8,addtwins,TRUE,FALSE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=FALSE|comp=additive,
+full,highgen,9,2,0.8,addtwins,TRUE,TRUE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=TRUE|comp=additive,
+full,highgen,9,2,0.8,merging,FALSE,FALSE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,
+full,highgen,9,2,0.8,merging,FALSE,TRUE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=merging|beta=FALSE|sparse=TRUE|comp=additive,
+full,highgen,9,2,0.8,merging,TRUE,FALSE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=merging|beta=TRUE|sparse=FALSE|comp=additive,
+full,highgen,9,2,0.8,merging,TRUE,TRUE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=merging|beta=TRUE|sparse=TRUE|comp=additive,
+full,highgen,9,2,0.8,NULL,FALSE,FALSE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,
+full,highgen,9,2,0.8,NULL,FALSE,TRUE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=NULL|beta=FALSE|sparse=TRUE|comp=additive,
+full,highgen,9,2,0.8,NULL,TRUE,FALSE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,
+full,highgen,9,2,0.8,NULL,TRUE,TRUE,3,0.5,TRUE,additive,full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,
+full,midgen,6,2,0.8,addtwins,FALSE,FALSE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=FALSE|comp=additive,
+full,midgen,6,2,0.8,addtwins,FALSE,TRUE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=TRUE|comp=additive,
+full,midgen,6,2,0.8,addtwins,TRUE,FALSE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=FALSE|comp=additive,
+full,midgen,6,2,0.8,addtwins,TRUE,TRUE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=TRUE|comp=additive,
+full,midgen,6,2,0.8,merging,FALSE,FALSE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,
+full,midgen,6,2,0.8,merging,FALSE,TRUE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=TRUE|comp=additive,
+full,midgen,6,2,0.8,merging,TRUE,FALSE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=FALSE|comp=additive,
+full,midgen,6,2,0.8,merging,TRUE,TRUE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=TRUE|comp=additive,
+full,midgen,6,2,0.8,NULL,FALSE,FALSE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,
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+full,midgen,6,2,0.8,NULL,TRUE,FALSE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,
+full,midgen,6,2,0.8,NULL,TRUE,TRUE,3,0.5,TRUE,additive,full|ped=midgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,
diff --git a/data-raw/ped2com_benchmark_end_time.txt b/data-raw/ped2com_benchmark_end_time.txt
new file mode 100644
index 00000000..ae7f71ae
--- /dev/null
+++ b/data-raw/ped2com_benchmark_end_time.txt
@@ -0,0 +1 @@
+2026-02-16 10:47:50.923385
diff --git a/data-raw/ped2com_benchmark_results.csv b/data-raw/ped2com_benchmark_results.csv
new file mode 100644
index 00000000..c26e7302
--- /dev/null
+++ b/data-raw/ped2com_benchmark_results.csv
@@ -0,0 +1,241 @@
+expr,time,label,scenario,ped_label,Ngen_total,gen_twin,marR,twin_method,beta,sparse_matrix,kpc,sexR,sim_beta,component,gen_factor
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+full|ped=highgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,253422200,full|ped=highgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,full,highgen,9,2,0.8,merging,FALSE,FALSE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=TRUE|comp=additive,11325500,full|ped=midgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=TRUE|comp=additive,full,midgen,6,2,0.8,addtwins,TRUE,TRUE,3,0.5,TRUE,additive,6 gen
+full|ped=highgen|marR=0.8|twin=merging|beta=FALSE|sparse=TRUE|comp=additive,498453800,full|ped=highgen|marR=0.8|twin=merging|beta=FALSE|sparse=TRUE|comp=additive,full,highgen,9,2,0.8,merging,FALSE,TRUE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=TRUE|comp=additive,6816400,full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=TRUE|comp=additive,full,midgen,6,2,0.8,NULL,FALSE,TRUE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,9532900,full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,merging,FALSE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,7317400,full|ped=midgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,NULL,TRUE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=highgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=TRUE|comp=additive,815704200,full|ped=highgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=TRUE|comp=additive,full,highgen,9,2,0.8,addtwins,FALSE,TRUE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=TRUE|comp=additive,8478700,full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=TRUE|comp=additive,full,midgen,6,2,0.8,merging,TRUE,TRUE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,7668400,full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,merging,FALSE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,189231300,full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,full,highgen,9,2,0.8,NULL,TRUE,TRUE,3,0.5,TRUE,additive,9 gen
+full|ped=highgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=TRUE|comp=additive,970275100,full|ped=highgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=TRUE|comp=additive,full,highgen,9,2,0.8,addtwins,FALSE,TRUE,3,0.5,TRUE,additive,9 gen
+full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,224921500,full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,full,highgen,9,2,0.8,NULL,TRUE,TRUE,3,0.5,TRUE,additive,9 gen
+full|ped=highgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=TRUE|comp=additive,823923000,full|ped=highgen|marR=0.8|twin=addtwins|beta=TRUE|sparse=TRUE|comp=additive,full,highgen,9,2,0.8,addtwins,TRUE,TRUE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=TRUE|comp=additive,8553200,full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=TRUE|comp=additive,full,midgen,6,2,0.8,merging,TRUE,TRUE,3,0.5,TRUE,additive,6 gen
+full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,203815400,full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,full,highgen,9,2,0.8,NULL,TRUE,FALSE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=TRUE|comp=additive,8441500,full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=TRUE|comp=additive,full,midgen,6,2,0.8,merging,FALSE,TRUE,3,0.5,TRUE,additive,6 gen
+full|ped=highgen|marR=0.8|twin=merging|beta=TRUE|sparse=FALSE|comp=additive,239606300,full|ped=highgen|marR=0.8|twin=merging|beta=TRUE|sparse=FALSE|comp=additive,full,highgen,9,2,0.8,merging,TRUE,FALSE,3,0.5,TRUE,additive,9 gen
+full|ped=highgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,434145700,full|ped=highgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,full,highgen,9,2,0.8,NULL,FALSE,FALSE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,7041200,full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,NULL,FALSE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,7063600,full|ped=midgen|marR=0.8|twin=merging|beta=FALSE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,merging,FALSE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,206283600,full|ped=highgen|marR=0.8|twin=NULL|beta=TRUE|sparse=FALSE|comp=additive,full,highgen,9,2,0.8,NULL,TRUE,FALSE,3,0.5,TRUE,additive,9 gen
+full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,6966300,full|ped=midgen|marR=0.8|twin=NULL|beta=FALSE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,NULL,FALSE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,6370100,full|ped=midgen|marR=0.8|twin=NULL|beta=TRUE|sparse=TRUE|comp=additive,full,midgen,6,2,0.8,NULL,TRUE,TRUE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=FALSE|comp=additive,6395900,full|ped=midgen|marR=0.8|twin=merging|beta=TRUE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,merging,TRUE,FALSE,3,0.5,TRUE,additive,6 gen
+full|ped=midgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=FALSE|comp=additive,7256300,full|ped=midgen|marR=0.8|twin=addtwins|beta=FALSE|sparse=FALSE|comp=additive,full,midgen,6,2,0.8,addtwins,FALSE,FALSE,3,0.5,TRUE,additive,6 gen
diff --git a/data-raw/ped2com_benchmark_start_time.txt b/data-raw/ped2com_benchmark_start_time.txt
new file mode 100644
index 00000000..91c3edb0
--- /dev/null
+++ b/data-raw/ped2com_benchmark_start_time.txt
@@ -0,0 +1 @@
+2026-02-16 10:46:51.927423
diff --git a/data-raw/ped2com_benchmark_summary.csv b/data-raw/ped2com_benchmark_summary.csv
new file mode 100644
index 00000000..5fd17eb4
--- /dev/null
+++ b/data-raw/ped2com_benchmark_summary.csv
@@ -0,0 +1,13 @@
+ped_label,twin_method,sparse_matrix,median_time_ms,mean_time_ms,var_time_ms,sd_time_ms,se_time_ms,n_time
+midgen,NULL,FALSE,7.08545,7.065175,0.38096159250000006,0.6172208620097024,0.13801478045847118,20
+midgen,NULL,TRUE,7.010350000000001,6.97651,0.3269079230526316,0.5717586230680143,0.1278491147901759,20
+midgen,addtwins,FALSE,8.371,8.478245,1.852214922605263,1.360961029054566,0.30432013756940757,20
+midgen,addtwins,TRUE,11.5961,12.25093,13.742667178000001,3.7071103541707524,0.8289350752019123,20
+midgen,merging,FALSE,7.703200000000001,7.761935,0.6603589255526314,0.81262471384559265,0.18170841003550597,20
+midgen,merging,TRUE,9.5205,11.184985,48.470337688710515,6.962064183035841,1.556764877698468,20
+highgen,NULL,FALSE,225.3165,243.54288,3892.258395726947,62.38796675423031,13.950373464045589,20
+highgen,NULL,TRUE,194.2853,214.36891,3924.8415531188416,62.648555874168736,14.008642962683505,20
+highgen,addtwins,FALSE,261.08675,315.74309,9358.837543218842,96.74108508394374,21.631964246478915,20
+highgen,addtwins,TRUE,1081.53665,1098.186765,40060.529971101336,200.15126772294332,44.75518404112608,20
+highgen,merging,FALSE,253.68965,307.289225,7715.235275696711,87.83641201515867,19.640818816557406,20
+highgen,merging,TRUE,691.4244,704.852615,29254.359534471863,171.03905850557018,38.24549616260185,20
diff --git a/data-raw/twin_methods_sparse.png b/data-raw/twin_methods_sparse.png
new file mode 100644
index 00000000..d69e5a0a
Binary files /dev/null and b/data-raw/twin_methods_sparse.png differ
diff --git a/man/adjustKidsPerCouple.Rd b/man/adjustKidsPerCouple.Rd
index bf5b8192..dbe7e500 100644
--- a/man/adjustKidsPerCouple.Rd
+++ b/man/adjustKidsPerCouple.Rd
@@ -21,7 +21,18 @@ value is 3. Returns an error when kpc equals 1.}
 generated from a poisson distribution with mean kpc. If FALSE, the number of
 kids per mate will be fixed at kpc.}
 
-\item{beta}{logical. If TRUE, use the optimized version of the algorithm.}
+\item{beta}{logical or character. Controls which algorithm version to use:
+\itemize{
+  \item{\code{FALSE}, \code{"base"}, or \code{"original"} (default): Use the original algorithm.
+        Slower but ensures exact reproducibility with set.seed().}
+  \item{\code{TRUE} or \code{"optimized"}: Use the optimized algorithm with 4-5x speedup.
+        Produces statistically equivalent results but not identical to base version
+        due to different random number consumption. Recommended for large simulations
+        where speed matters more than exact reproducibility.}
+}
+Note: Both versions are mathematically correct and produce valid pedigrees with the
+same statistical properties (sex ratios, mating rates, etc.). The optimized version
+uses vectorized operations instead of loops, making it much faster for large pedigrees.}
 }
 \value{
 A numeric vector with the generated or adjusted number of kids per couple.
diff --git a/man/buildBetweenGenerations.Rd b/man/buildBetweenGenerations.Rd
index fa87604e..f28622b2 100644
--- a/man/buildBetweenGenerations.Rd
+++ b/man/buildBetweenGenerations.Rd
@@ -62,7 +62,18 @@ kids per mate will be fixed at kpc.}
 
 \item{code_female}{The value to use for females. Default is "F"}
 
-\item{beta}{logical. If TRUE, use the optimized version of the algorithm.}
+\item{beta}{logical or character. Controls which algorithm version to use:
+\itemize{
+  \item{\code{FALSE}, \code{"base"}, or \code{"original"} (default): Use the original algorithm.
+        Slower but ensures exact reproducibility with set.seed().}
+  \item{\code{TRUE} or \code{"optimized"}: Use the optimized algorithm with 4-5x speedup.
+        Produces statistically equivalent results but not identical to base version
+        due to different random number consumption. Recommended for large simulations
+        where speed matters more than exact reproducibility.}
+}
+Note: Both versions are mathematically correct and produce valid pedigrees with the
+same statistical properties (sex ratios, mating rates, etc.). The optimized version
+uses vectorized operations instead of loops, making it much faster for large pedigrees.}
 }
 \value{
 The function updates the `df_Fam` data frame in place, adding or modifying columns related to parental and offspring status,
diff --git a/man/buildWithinGenerations.Rd b/man/buildWithinGenerations.Rd
index 905e42ad..cbddf27a 100644
--- a/man/buildWithinGenerations.Rd
+++ b/man/buildWithinGenerations.Rd
@@ -20,7 +20,18 @@ buildWithinGenerations(
 )
 }
 \arguments{
-\item{beta}{logical. If TRUE, use the optimized version of the algorithm.}
+\item{beta}{logical or character. Controls which algorithm version to use:
+\itemize{
+  \item{\code{FALSE}, \code{"base"}, or \code{"original"} (default): Use the original algorithm.
+        Slower but ensures exact reproducibility with set.seed().}
+  \item{\code{TRUE} or \code{"optimized"}: Use the optimized algorithm with 4-5x speedup.
+        Produces statistically equivalent results but not identical to base version
+        due to different random number consumption. Recommended for large simulations
+        where speed matters more than exact reproducibility.}
+}
+Note: Both versions are mathematically correct and produce valid pedigrees with the
+same statistical properties (sex ratios, mating rates, etc.). The optimized version
+uses vectorized operations instead of loops, making it much faster for large pedigrees.}
 
 \item{sizeGens}{A numeric vector containing the sizes of each generation within the pedigree.}
 
diff --git a/man/dot-adjBeta.Rd b/man/dot-adjBeta.Rd
index 5b962868..3c572806 100644
--- a/man/dot-adjBeta.Rd
+++ b/man/dot-adjBeta.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/constructAdjacency.R
 \name{.adjBeta}
 \alias{.adjBeta}
-\title{Construct Adjacency Matrix for Parent-Child Relationships Using Beta Method
+\title{Construct Adjacency Matrix for Parent-Child Relationships Using Beta Methods
 This function constructs an adjacency matrix for parent-child relationships
 using a method in beta testing. It identifies parent-child pairs based on the
 specified component of relatedness.}
@@ -60,7 +60,7 @@ specified component of relatedness.}
 \item{...}{additional arguments to be passed to \code{\link{ped2com}}}
 }
 \description{
-Construct Adjacency Matrix for Parent-Child Relationships Using Beta Method
+Construct Adjacency Matrix for Parent-Child Relationships Using Beta Methods
 This function constructs an adjacency matrix for parent-child relationships
 using a method in beta testing. It identifies parent-child pairs based on the
 specified component of relatedness.
diff --git a/man/dropLink.Rd b/man/dropLink.Rd
index 668d9e82..2753587d 100644
--- a/man/dropLink.Rd
+++ b/man/dropLink.Rd
@@ -9,7 +9,8 @@ dropLink(
   ID_drop = NA_integer_,
   gen_drop = 2,
   sex_drop = NA_character_,
-  n_drop = 1
+  n_drop = 1,
+  verbose = FALSE
 )
 }
 \arguments{
@@ -22,6 +23,8 @@ dropLink(
 \item{sex_drop}{the biological sex of the randomly dropped person.}
 
 \item{n_drop}{the number of times the mutation happens.}
+
+\item{verbose}{logical.  If TRUE, print progress through stages of algorithm}
 }
 \value{
 a pedigree with the dropped person's `dadID` and `momID` set to NA.
diff --git a/man/findMZtwins.Rd b/man/findMZtwins.Rd
new file mode 100644
index 00000000..39a361fd
--- /dev/null
+++ b/man/findMZtwins.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/helpTwins.R
+\name{findMZtwins}
+\alias{findMZtwins}
+\title{Find MZ twin pair_rows in a pedigree}
+\usage{
+findMZtwins(
+  ped,
+  verbose = FALSE,
+  returnRows = TRUE,
+  returnIDs = FALSE,
+  returnAsList = TRUE,
+  beta = FALSE
+)
+}
+\arguments{
+\item{ped}{A pedigree data.frame with columns \code{ID} and \code{twinID}.
+Optionally a \code{zygosity} column; when present only pair_rows where both
+members have \code{zygosity == "MZ"} are used.}
+
+\item{verbose}{logical. If TRUE, print progress messages.}
+
+\item{returnRows}{logical. If TRUE, return the row indices of the twin pair_rows instead of IDs.}
+
+\item{returnIDs}{logical. If TRUE, return the IDs of the twin pair_rows instead of row indices.}
+
+\item{returnAsList}{logical. If TRUE, return results as a list of vectors
+(default). If FALSE, return results as a data.frame with separate columns for each twin's ID and row index.
+@param beta logical. If TRUE, use an optimized approach with O(1) lookups for large pedigrees. If FALSE (default), use a simpler approach that may be less efficient for large pedigrees.}
+}
+\value{
+A list of length-2 integer vectors \code{c(idx1, idx2)} giving the
+  row indices of each MZ pair in the pedigree, or \code{NULL} if none found.
+}
+\description{
+Identifies MZ twin pair_rows from the \code{twinID} column and returns their
+row indices.  These indices are used later to merge the twins' columns in
+the \code{r2} matrix before \code{tcrossprod}, which correctly produces
+relatedness 1 between MZ co-twins with no diagonal or downstream artifacts.
+}
+\keyword{internal}
diff --git a/man/fuseTwins.Rd b/man/fuseTwins.Rd
new file mode 100644
index 00000000..42eb49fa
--- /dev/null
+++ b/man/fuseTwins.Rd
@@ -0,0 +1,39 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/helpTwins.R
+\name{fuseTwins}
+\alias{fuseTwins}
+\title{Fuse MZ twin pairs in a pedigree dataset for path tracing
+This function identifies MZ twin pairs in the pedigree dataset and merges their IDs for path tracing purposes. The second twin in each pair is made a founder (with NA parents), and all children of the second twin are redirected to the first twin. This allows for correct relatedness calculations without diagonal or downstream artifacts.}
+\usage{
+fuseTwins(
+  ped,
+  df_twins = NULL,
+  mz_id_pairs = NULL,
+  mz_row_pairs = NULL,
+  config = list(verbose = FALSE),
+  test_df_twins = FALSE,
+  beta = FALSE
+)
+}
+\arguments{
+\item{ped}{A pedigree data.frame with columns \code{ID}, \code{momID}, \code{dadID}, and optionally \code{twinID} and \code{zygosity}. The function will look for MZ twin pairs based on the \code{twinID} column and optionally restrict to MZ pairs if a \code{zygosity} column is present.}
+
+\item{df_twins}{Optional data frame with columns \code{twin1_id}, \code{twin2_id}, \code{twin1_row}, and \code{twin2_row} specifying the IDs and row indices of MZ twin pairs to fuse. If provided, this will be used instead of automatically identifying MZ twins from the \code{twinID} column. If this parameter is provided, it takes precedence over \code{mz_id_pairs} and \code{mz_row_pairs}. If \code{test_df_twins} is TRUE, this data frame will be returned for testing purposes instead of performing the fusion.}
+
+\item{mz_id_pairs}{Optional list of length-2 character vectors specifying the IDs of MZ twin pairs to fuse. If provided, this will be used instead of automatically identifying MZ twins from the \code{twinID} column. Each element should be a character vector of length 2, e.g. \code{list(c("ID1", "ID2"), c("ID3", "ID4"))}.}
+
+\item{mz_row_pairs}{Optional list of length-2 integer vectors specifying the row indices of MZ twin pairs to fuse. If provided, this will be used instead of automatically identifying MZ twins from the \code{twinID} column. Each element should be an integer vector of length 2, e.g. \code{list(c(1, 2), c(3, 4))}.}
+
+\item{config}{A list of configuration options.}
+
+\item{test_df_twins}{logical. If TRUE, return the data frame of twin pairs instead of the modified pedigree. Default is FALSE.}
+
+\item{beta}{logical. If TRUE, use an optimized approach with O(1) lookups for large pedigrees when identifying MZ twins. Default is FALSE.}
+}
+\value{
+A modified version of the input pedigree data.frame with MZ twin pairs fused for path tracing. If \code{test_df_twins} is TRUE, returns the data frame of identified twin pairs instead.
+}
+\description{
+Fuse MZ twin pairs in a pedigree dataset for path tracing
+This function identifies MZ twin pairs in the pedigree dataset and merges their IDs for path tracing purposes. The second twin in each pair is made a founder (with NA parents), and all children of the second twin are redirected to the first twin. This allows for correct relatedness calculations without diagonal or downstream artifacts.
+}
diff --git a/man/isTwin.Rd b/man/isTwin.Rd
new file mode 100644
index 00000000..e2f3e887
--- /dev/null
+++ b/man/isTwin.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/helpTwins.R
+\name{isTwin}
+\alias{isTwin}
+\title{Determine isTwin Status}
+\usage{
+isTwin(ped)
+}
+\arguments{
+\item{ped}{pedigree data frame}
+}
+\value{
+isTwin 'S' matrix
+}
+\description{
+Determine isTwin Status
+}
+\keyword{internal}
diff --git a/man/makeInbreeding.Rd b/man/makeInbreeding.Rd
index 1540b170..d43200c5 100644
--- a/man/makeInbreeding.Rd
+++ b/man/makeInbreeding.Rd
@@ -10,7 +10,8 @@ makeInbreeding(
   ID_mate2 = NA_integer_,
   verbose = FALSE,
   gen_inbred = 2,
-  type_inbred = "sib"
+  type_inbred = "sib",
+  prefer_unmated = FALSE
 )
 }
 \arguments{
@@ -25,6 +26,8 @@ makeInbreeding(
 \item{gen_inbred}{A vector of \code{generation} of the twin to be imputed.}
 
 \item{type_inbred}{A character vector indicating the type of inbreeding. "sib" for sibling inbreeding and "cousin" for cousin inbreeding.}
+
+\item{prefer_unmated}{A logical indicating whether to prefer unmated siblings when automatically selecting inbred mates. Default is FALSE, which means the function will consider all siblings regardless of their mating status.}
 }
 \value{
 Returns a \code{data.frame} with some inbred mates.
diff --git a/man/makeTwins.Rd b/man/makeTwins.Rd
index cb9733e5..dc4f3ee5 100644
--- a/man/makeTwins.Rd
+++ b/man/makeTwins.Rd
@@ -10,7 +10,8 @@ makeTwins(
   ID_twin2 = NA_integer_,
   gen_twin = 2,
   verbose = FALSE,
-  zygosity = "MZ"
+  zygosity = "MZ",
+  twin_sex = "R"
 )
 }
 \arguments{
@@ -25,6 +26,8 @@ makeTwins(
 \item{verbose}{logical.  If TRUE, print progress through stages of algorithm}
 
 \item{zygosity}{A character string indicating the zygosity of the twins. Default is "MZ" for monozygotic twins.}
+
+\item{twin_sex}{A character string indicating the sex of the twins. Default is randomly assigned ("R"). If specified, it should be either "M" or "F"}
 }
 \value{
 Returns a \code{data.frame} with MZ twins information added as a new column.
diff --git a/man/ped2add.Rd b/man/ped2add.Rd
index dc2fee88..0a08cd09 100644
--- a/man/ped2add.Rd
+++ b/man/ped2add.Rd
@@ -21,6 +21,8 @@ ped2add(
   save_rate_parlist = 1e+05 * save_rate,
   save_path = "checkpoint/",
   compress = TRUE,
+  mz_twins = FALSE,
+  mz_method = "addtwins",
   ...
 )
 }
@@ -57,6 +59,10 @@ ped2add(
 
 \item{compress}{logical. If TRUE, use compression when saving the checkpoint files.  Defaults to TRUE.}
 
+\item{mz_twins}{logical. If TRUE, merge MZ co-twin columns in the r2 matrix before tcrossprod so that MZ twins are coded with relatedness 1 instead of 0.5. Twin pairs are identified from the \code{twinID} column. When a \code{zygosity} column is also present, only pairs where both members have \code{zygosity == "MZ"} are used; otherwise all \code{twinID} pairs are assumed to be MZ. Defaults to FALSE.}
+
+\item{mz_method}{character. The method to handle MZ twins.  Options are "merging" (default) or "addtwins".  "addtwins" adds the twin2 column to the twin1 column before tcrossprod so that all relatedness flows through a single source, then leaves the twin2 column as zero and relies on the fact that the row/col names are the same to copy the values back to twin2 after tcrossprod.  "merging" merges the twin2 column into the twin1 column before tcrossprod and then copies the values back to twin2 after tcrossprod so that both twins appear in the final matrix.}
+
 \item{...}{additional arguments to be passed to \code{\link{ped2com}}}
 }
 \description{
diff --git a/man/ped2com.Rd b/man/ped2com.Rd
index f34d6022..dc57cdb7 100644
--- a/man/ped2com.Rd
+++ b/man/ped2com.Rd
@@ -15,7 +15,7 @@ ped2com(
   standardize_colnames = TRUE,
   transpose_method = "tcrossprod",
   adjacency_method = "direct",
-  isChild_method = "classic",
+  isChild_method = "partialparent",
   saveable = FALSE,
   resume = FALSE,
   save_rate = 5,
@@ -25,6 +25,9 @@ ped2com(
   save_path = "checkpoint/",
   adjBeta_method = NULL,
   compress = TRUE,
+  mz_twins = TRUE,
+  mz_method = "addtwins",
+  beta = FALSE,
   ...
 )
 }
@@ -69,6 +72,12 @@ ped2com(
 
 \item{compress}{logical. If TRUE, use compression when saving the checkpoint files.  Defaults to TRUE.}
 
+\item{mz_twins}{logical. If TRUE, merge MZ co-twin columns in the r2 matrix before tcrossprod so that MZ twins are coded with relatedness 1 instead of 0.5. Twin pairs are identified from the \code{twinID} column. When a \code{zygosity} column is also present, only pairs where both members have \code{zygosity == "MZ"} are used; otherwise all \code{twinID} pairs are assumed to be MZ. Defaults to FALSE.}
+
+\item{mz_method}{character. The method to handle MZ twins.  Options are "merging" (default) or "addtwins".  "addtwins" adds the twin2 column to the twin1 column before tcrossprod so that all relatedness flows through a single source, then leaves the twin2 column as zero and relies on the fact that the row/col names are the same to copy the values back to twin2 after tcrossprod.  "merging" merges the twin2 column into the twin1 column before tcrossprod and then copies the values back to twin2 after tcrossprod so that both twins appear in the final matrix.}
+
+\item{beta}{logical. Used for benchmarking}
+
 \item{...}{additional arguments to be passed to \code{\link{ped2com}}}
 }
 \description{
diff --git a/man/ped2fam.Rd b/man/ped2fam.Rd
index 3052e568..b3cf1e07 100644
--- a/man/ped2fam.Rd
+++ b/man/ped2fam.Rd
@@ -10,6 +10,7 @@ ped2fam(
   momID = "momID",
   dadID = "dadID",
   famID = "famID",
+  twinID = "twinID",
   ...
 )
 }
@@ -24,6 +25,8 @@ ped2fam(
 
 \item{famID}{character.  Name of the column to be created in ped for the family ID variable}
 
+\item{twinID}{character.  Name of the column in ped for the twin ID variable, if applicable}
+
 \item{...}{additional arguments to be passed to \code{\link{ped2com}}}
 }
 \value{
diff --git a/man/ped2graph.Rd b/man/ped2graph.Rd
index 5e7ac7b1..022af3c7 100755
--- a/man/ped2graph.Rd
+++ b/man/ped2graph.Rd
@@ -9,6 +9,7 @@ ped2graph(
   personID = "ID",
   momID = "momID",
   dadID = "dadID",
+  twinID = "twinID",
   directed = TRUE,
   adjacent = c("parents", "mothers", "fathers"),
   ...
@@ -23,6 +24,8 @@ ped2graph(
 
 \item{dadID}{character.  Name of the column in ped for the father ID variable}
 
+\item{twinID}{character.  Name of the column in ped for the twin ID variable, if applicable}
+
 \item{directed}{Logical scalar. Default is TRUE. Indicates whether or not to create a directed graph.}
 
 \item{adjacent}{Character.  Relationship that defines adjacency in the graph: parents, mothers, or fathers}
diff --git a/man/ped2maternal.Rd b/man/ped2maternal.Rd
index 03e02311..1f3bcb2e 100755
--- a/man/ped2maternal.Rd
+++ b/man/ped2maternal.Rd
@@ -10,6 +10,7 @@ ped2maternal(
   momID = "momID",
   dadID = "dadID",
   matID = "matID",
+  twinID = "twinID",
   ...
 )
 }
@@ -24,6 +25,8 @@ ped2maternal(
 
 \item{matID}{Character. Maternal line ID variable to be created and added to the pedigree}
 
+\item{twinID}{character.  Name of the column in ped for the twin ID variable, if applicable}
+
 \item{...}{additional arguments to be passed to \code{\link{ped2com}}}
 }
 \description{
diff --git a/man/ped2paternal.Rd b/man/ped2paternal.Rd
index e893ec03..7fac27b9 100755
--- a/man/ped2paternal.Rd
+++ b/man/ped2paternal.Rd
@@ -10,6 +10,7 @@ ped2paternal(
   momID = "momID",
   dadID = "dadID",
   patID = "patID",
+  twinID = "twinID",
   ...
 )
 }
@@ -24,6 +25,8 @@ ped2paternal(
 
 \item{patID}{Character.  Paternal line ID variable to be created and added to the pedigree}
 
+\item{twinID}{character.  Name of the column in ped for the twin ID variable, if applicable}
+
 \item{...}{additional arguments to be passed to \code{\link{ped2com}}}
 }
 \description{
diff --git a/man/simulatePedigree.Rd b/man/simulatePedigree.Rd
index fa87632e..d56ab72c 100644
--- a/man/simulatePedigree.Rd
+++ b/man/simulatePedigree.Rd
@@ -76,7 +76,18 @@ current version.}
 \item{fam_shift}{An integer to shift the person ID. Default is 1L.
 This is useful when simulating multiple pedigrees to avoid ID conflicts.}
 
-\item{beta}{logical. If TRUE, use the optimized version of the algorithm.}
+\item{beta}{logical or character. Controls which algorithm version to use:
+\itemize{
+  \item{\code{FALSE}, \code{"base"}, or \code{"original"} (default): Use the original algorithm.
+        Slower but ensures exact reproducibility with set.seed().}
+  \item{\code{TRUE} or \code{"optimized"}: Use the optimized algorithm with 4-5x speedup.
+        Produces statistically equivalent results but not identical to base version
+        due to different random number consumption. Recommended for large simulations
+        where speed matters more than exact reproducibility.}
+}
+Note: Both versions are mathematically correct and produce valid pedigrees with the
+same statistical properties (sex ratios, mating rates, etc.). The optimized version
+uses vectorized operations instead of loops, making it much faster for large pedigrees.}
 
 \item{...}{Additional arguments to be passed to other functions.}
 }
diff --git a/tests/testthat/test-buildComponent.R b/tests/testthat/test-buildComponent.R
index b00fa039..d863b0f7 100644
--- a/tests/testthat/test-buildComponent.R
+++ b/tests/testthat/test-buildComponent.R
@@ -1,3 +1,245 @@
+test_that("MZ twins coded at relatedness 1 via twinID column", {
+  # Simple pedigree: two parents and two MZ twin children
+  ped <- potter
+
+  mz_method_opts <- c("addtwins", "merging")
+
+  for (mz_method in mz_method_opts) {
+    #  mz_method <- "merging" # "addtwins"
+    # Without mz_twins: siblings get 0.5
+    r_no_mz <- ped2add(ped, mz_twins = FALSE, sparse = FALSE, mz_method = mz_method)
+    expect_equal(r_no_mz["12", "13"], 0.5)
+    expect_equal(r_no_mz["13", "12"], 0.5)
+
+    # With mz_twins: MZ twins get 1.0
+    r_mz <- ped2add(ped, mz_twins = TRUE, sparse = FALSE, mz_method = mz_method)
+    expect_equal(r_mz["12", "13"], 1.0)
+    expect_equal(r_mz["13", "12"], 1.0)
+
+    # Self-relatedness should still be 1
+    expect_equal(r_mz["12", "12"], 1.0)
+    expect_equal(r_mz["13", "13"], 1.0)
+
+    # Parent-child relatedness unchanged
+    expect_equal(r_mz["12", "9"], 0.5)
+    expect_equal(r_mz["13", "9"], 0.5)
+    expect_equal(r_mz["12", "10"], 0.5)
+    expect_equal(r_mz["13", "10"], 0.5)
+  }
+  ped_kids <- potter
+
+  # Add a child to one of the MZ twins
+  ped_kids <- addPersonToPed(ped_kids, sex = 0, momID = NA, dadID = NA, personID = 31)
+  ped_kids <- addPersonToPed(ped_kids, sex = 0, momID = NA, dadID = NA, personID = 32)
+  ped_kids <- addPersonToPed(ped_kids, sex = 0, momID = 31, dadID = 12, personID = 33)
+  ped_kids <- addPersonToPed(ped_kids, sex = 0, momID = 32, dadID = 13, personID = 34)
+  ped_kids <- addPersonToPed(ped_kids, sex = 0, momID = 31, dadID = 13, personID = 35)
+
+  for (mz_method in mz_method_opts) {
+    r_kids <- ped2add(ped_kids, mz_twins = TRUE, sparse = FALSE, mz_method = mz_method)
+    # Child of twin1 (ID=31) should be 0.5 to twin1 (parent)
+    expect_equal(r_kids["33", "12"], 0.5)
+    # Child of twin1 should ALSO be 0.5 to twin2 (genetically identical to parent)
+    expect_equal(r_kids["33", "13"], 0.5)
+    # Child of twin2 (ID=32) should be 0.5 to twin
+    expect_equal(r_kids["34", "13"], 0.5)
+    # Child of twin2 should ALSO be 0.5 to twin1 (genetically identical to parent)
+    expect_equal(r_kids["34", "12"], 0.5)
+
+    # different moms should be 0.25 with different mz twin dads
+    expect_equal(r_kids["34", "33"], 0.25)
+    expect_equal(r_kids["34", "35"], 0.25)
+
+    # same mom, different mz twin dads should be 0.5
+    expect_equal(r_kids["33", "35"], 0.5)
+  }
+
+  r_mz1 <- ped2add(ped_kids, mz_twins = TRUE, sparse = FALSE, mz_method = mz_method_opts[1])
+
+  r_mz2 <- ped2add(ped_kids, mz_twins = TRUE, sparse = FALSE, mz_method = mz_method_opts[2])
+
+  expect_equal(r_mz1, r_mz2)
+})
+
+
+test_that("MZ twins coded at relatedness 1 via twinID column (complex pedigree)", {
+  set.seed(1667)
+  Ngen <- 5
+  kpc <- 4
+  sexR <- .50 # sometimes fails above .5
+  marR <- 1
+
+  gen_twin <- 3 # max(c(floor(Ngen / 2) - 1, 2))
+
+  # create base pedigree with twins at gen_twin
+
+  df_midgen_base <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR, beta = TRUE) |>
+    makeTwins(gen_twin = gen_twin)
+
+  twinIDS <-  findMZtwins(df_midgen_base, returnRows =  FALSE,
+    returnIDs = TRUE, returnAsList = FALSE)
+
+  moms_of_twins <- df_midgen_base$momID[df_midgen_base$ID %in% c(twinIDS$twin1_id, twinIDS$twin2_id)] %>% unique()
+  dads_of_twins <- df_midgen_base$dadID[df_midgen_base$ID %in% c(twinIDS$twin1_id, twinIDS$twin2_id)] %>% unique()
+  parents_of_twins <- unique(c(moms_of_twins, dads_of_twins))
+
+  grandmothers_of_twins <- df_midgen_base$momID[df_midgen_base$ID %in% parents_of_twins] %>% unique()
+  grandfathers_of_twins <- df_midgen_base$dadID[df_midgen_base$ID %in% parents_of_twins] %>% unique()
+  grandfathers_of_twins <- grandfathers_of_twins[!is.na(grandfathers_of_twins)]
+  grandmothers_of_twins <- grandmothers_of_twins[!is.na(grandmothers_of_twins)]
+  grandparents_of_twins <- unique(c(grandmothers_of_twins, grandfathers_of_twins))
+
+  female_children_of_twins <- df_midgen_base$ID[(df_midgen_base$momID %in% c(twinIDS$twin1_id, twinIDS$twin2_id) | df_midgen_base$dadID %in% c(twinIDS$twin1_id, twinIDS$twin2_id)) & df_midgen_base$sex == "F"] %>% unique()
+
+  male_children_of_twins <- df_midgen_base$ID[(df_midgen_base$momID %in% c(twinIDS$twin1_id, twinIDS$twin2_id) | df_midgen_base$dadID %in% c(twinIDS$twin1_id, twinIDS$twin2_id)) & df_midgen_base$sex == "M"] %>% unique()
+
+  children_of_twins <- c(male_children_of_twins, female_children_of_twins)
+
+  df_midgen_below <- df_midgen_base |> makeInbreeding(ID_mate1 = male_children_of_twins[1], ID_mate2 = female_children_of_twins[length(female_children_of_twins)])
+
+  df_midgen_above <- df_midgen_base
+
+  df_midgen_above$momID[df_midgen_above$ID %in% parents_of_twins] <- grandmothers_of_twins[1]
+  df_midgen_above$dadID[df_midgen_above$ID %in% parents_of_twins] <- grandfathers_of_twins[1]
+
+  if (FALSE) {
+    df_midgen_below %>% rename(personID = ID) %>%
+      ggpedigree::ggPedigreeInteractive(config = list(code_male = "M", focal_fill_personID = twinIDS$twin1_id,
+        focal_fill_include = TRUE,
+        sex_color_include = FALSE))
+
+  }
+  for (df_midgen in list(df_midgen_base, df_midgen_below, df_midgen_above)) {
+    r_mz1 <- df_midgen |>
+      ped2add(mz_method = "merging", mz_twins = TRUE)
+    r_mz2 <- df_midgen |>
+      ped2add(mz_method = "addtwins", mz_twins = TRUE)
+
+    r_mz3 <- df_midgen |>
+      ped2add(mz_twins = FALSE)
+
+    # which rows are the twins
+    twin_rows <- which(!is.na(df_midgen$twinID))
+    child_rows <- which(df_midgen$momID %in% df_midgen$ID[twin_rows] | df_midgen$dadID %in% df_midgen$ID[twin_rows])
+
+    family_rows <- unique(c(twin_rows, child_rows))
+
+    expect_equal(sum(as.matrix(r_mz1[family_rows, family_rows]) - as.matrix(r_mz2[family_rows, family_rows])), 0)
+
+    expect_gt(sum(as.matrix(r_mz1[family_rows, family_rows])), sum(as.matrix(r_mz3[family_rows, family_rows])))
+
+    expect_gt(sum(as.matrix(r_mz2[family_rows, family_rows])), sum(as.matrix(r_mz3[family_rows, family_rows])))
+
+
+    r_mz1_ordered <- r_mz1[order(rownames(r_mz1)), order(colnames(r_mz1))]
+    r_mz2_ordered <- r_mz2[order(rownames(r_mz2)), order(colnames(r_mz2))]
+
+    expect_equal(sum(r_mz1_ordered - r_mz2_ordered), 0)
+
+    expect_equal(length(r_mz1@i), length(r_mz2@i))
+    expect_equal(length(r_mz1@x), length(r_mz2@x))
+    expect_equal(length(r_mz1@p), length(r_mz2@p))
+
+    expect_equal(length(r_mz1@i), length(r_mz3@i))
+    expect_equal(length(r_mz2@i), length(r_mz3@i))
+
+    expect_equal(length(r_mz1@x), length(r_mz3@x))
+    expect_equal(length(r_mz2@x), length(r_mz3@x))
+
+    expect_equal(length(r_mz1@p), length(r_mz3@p))
+    expect_equal(length(r_mz2@p), length(r_mz3@p))
+  }
+})
+
+test_that("MZ twins coded at relatedness 1 via twinID column (minimal data.frame)", {
+  # Simple pedigree: two parents and two MZ twin children
+  ped <- data.frame(
+    ID = c(1, 2, 3, 4),
+    momID = c(NA, NA, 2, 2),
+    dadID = c(NA, NA, 1, 1),
+    sex = c("M", "F", "M", "M"),
+    twinID = c(NA, NA, 4, 3),
+    zygosity = c(NA, NA, "MZ", "MZ")
+  )
+
+  # Without mz_twins: siblings get 0.5
+  r_no_mz <- ped2add(ped, mz_twins = FALSE, sparse = FALSE)
+  expect_equal(r_no_mz["3", "4"], 0.5)
+  expect_equal(r_no_mz["4", "3"], 0.5)
+
+  # With mz_twins: MZ twins get 1.0
+  r_mz <- ped2add(ped, mz_twins = TRUE, sparse = FALSE)
+  expect_equal(r_mz["3", "4"], 1.0)
+  expect_equal(r_mz["4", "3"], 1.0)
+
+  # Self-relatedness should still be 1
+  expect_equal(r_mz["3", "3"], 1.0)
+  expect_equal(r_mz["4", "4"], 1.0)
+
+  # Parent-child relatedness unchanged
+  expect_equal(r_mz["3", "1"], 0.5)
+  expect_equal(r_mz["4", "1"], 0.5)
+  expect_equal(r_mz["3", "2"], 0.5)
+  expect_equal(r_mz["4", "2"], 0.5)
+})
+
+test_that("MZ twins without zygosity column assumes all twinID pairs are MZ", {
+  ped <- data.frame(
+    ID = c(1, 2, 3, 4),
+    momID = c(NA, NA, 2, 2),
+    dadID = c(NA, NA, 1, 1),
+    sex = c("M", "F", "M", "M"),
+    twinID = c(NA, NA, 4, 3)
+  )
+
+  r_mz <- ped2add(ped, mz_twins = TRUE, sparse = FALSE)
+  expect_equal(r_mz["3", "4"], 1.0)
+  expect_equal(r_mz["4", "3"], 1.0)
+})
+
+test_that("DZ twins with zygosity column are NOT modified", {
+  ped <- data.frame(
+    ID = c(1, 2, 3, 4),
+    momID = c(NA, NA, 2, 2),
+    dadID = c(NA, NA, 1, 1),
+    sex = c("M", "F", "M", "F"),
+    twinID = c(NA, NA, 4, 3),
+    zygosity = c(NA, NA, "DZ", "DZ")
+  )
+
+  r_mz <- ped2add(ped, mz_twins = TRUE, sparse = FALSE)
+  # DZ twins remain at sibling relatedness = 0.5
+  expect_equal(r_mz["3", "4"], 0.5)
+  expect_equal(r_mz["4", "3"], 0.5)
+})
+
+test_that("MZ twins: downstream child relatedness is correct", {
+  # 3-generation pedigree: parents -> MZ twins -> twin2 has a child
+  ped <- data.frame(
+    ID = c(1, 2, 3, 4, 5, 6),
+    momID = c(NA, NA, 2, 2, NA, 4),
+    dadID = c(NA, NA, 1, 1, NA, 5),
+    sex = c("M", "F", "M", "M", "F", "M"),
+    twinID = c(NA, NA, 4, 3, NA, NA),
+    zygosity = c(NA, NA, "MZ", "MZ", NA, NA)
+  )
+
+  r_mz <- ped2add(ped, mz_twins = TRUE, sparse = FALSE)
+
+  # MZ twins at 1.0
+  expect_equal(r_mz["3", "4"], 1.0)
+
+  # Child of twin2 (ID=4) should be 0.5 to twin2 (parent)
+  expect_equal(r_mz["6", "4"], 0.5)
+
+  # Child of twin2 should ALSO be 0.5 to twin1 (genetically identical to parent)
+  expect_equal(r_mz["6", "3"], 0.5)
+
+  # Diagonal for both twins should be clean (no inflation)
+  expect_equal(r_mz["3", "3"], 1.0)
+  expect_equal(r_mz["4", "4"], 1.0)
+})
+
 test_that(".assignParentValue works", {
   expect_equal(.assignParentValue("generation"), .5)
   expect_equal(.assignParentValue("additive"), .5)
diff --git a/tests/testthat/test-helpTwins.R b/tests/testthat/test-helpTwins.R
new file mode 100644
index 00000000..e19643bb
--- /dev/null
+++ b/tests/testthat/test-helpTwins.R
@@ -0,0 +1,100 @@
+test_that("fuse twins behaves", {
+  # Simple pedigree: two parents and two MZ twin children
+  ped1 <- potter
+  ped1$ID <- ped1$personID
+  ped2 <- ped1
+  ped2$famID <- 2
+
+  ped2$ID <- ped2$personID + 100
+  ped2$momID <- ped2$momID + 100
+  ped2$dadID <- ped2$dadID + 100
+  ped2$twinID <- ped2$twinID + 100
+
+  ped <- rbind(ped1, ped2)
+  remove(ped2)
+  remove(ped1)
+  #  returnRows = TRUE,
+  # returnIDs = FALSE,
+  # returnAsList = TRUE
+
+  returnedRowsList <- findMZtwins(ped, returnRows = TRUE, returnIDs = FALSE, returnAsList = TRUE)
+  returnIDsList <- findMZtwins(ped, returnRows = FALSE, returnIDs = TRUE, returnAsList = TRUE)
+  returnedBothList <- findMZtwins(ped, returnRows = T, returnIDs = T, returnAsList = TRUE)
+
+  # no error should be thrown when running fuseTwins with any of the above outputs as arguments
+  expect_no_error(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = NULL,
+      mz_row_pairs = NULL
+    )
+  )
+
+  expect_no_error(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = NULL,
+      mz_row_pairs = returnedRowsList
+    )
+  )
+  expect_no_error(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = returnIDsList,
+      mz_row_pairs = NULL
+    )
+  )
+  expect_no_error(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = returnedBothList$pair_ids,
+      mz_row_pairs = returnedBothList$pair_rows
+    )
+  )
+
+
+  df_null <- tryCatch(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = NULL,
+      mz_row_pairs = NULL
+    ),
+    error = function(e) e
+  )
+
+
+  df_returnedRows <- tryCatch(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = NULL,
+      mz_row_pairs = returnedRowsList
+    ),
+    error = function(e) e
+  )
+
+
+  df_returnedIDs <- tryCatch(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = returnIDsList,
+      mz_row_pairs = NULL
+    ),
+    error = function(e) e
+  )
+
+
+  df_returnedBoth <- tryCatch(
+    fuseTwins(ped,
+      test_df_twins = TRUE,
+      mz_id_pairs = returnedBothList$pair_ids,
+      mz_row_pairs = returnedBothList$pair_rows
+    ),
+    error = function(e) e
+  )
+
+
+  expect_equal(df_returnedRows, df_returnedIDs)
+  expect_equal(df_returnedRows, df_returnedBoth)
+  expect_equal(df_returnedRows, df_null)
+  expect_equal(nrow(df_returnedRows), 2) # One pair of twins should returned
+})
diff --git a/tests/testthat/test-segmentPedigree.R b/tests/testthat/test-segmentPedigree.R
index a0f64e08..cf1cce02 100644
--- a/tests/testthat/test-segmentPedigree.R
+++ b/tests/testthat/test-segmentPedigree.R
@@ -1,9 +1,11 @@
 test_that("ped2fam is smart about string ids", {
   data(hazard)
+  # ped2fam should work with numeric IDs and produce numeric IDs
   ds_num <- ped2fam(hazard, famID = "newFamID")
   expect_true(is.numeric(ds_num$ID))
   expect_true(is.numeric(ds_num$newFamID))
   hazard$ID_og <- hazard$ID
+  # ped2fam should work with string IDs and produce string IDs
   hazard$ID <- paste0("ID", hazard$ID)
   hazard$dadID <- paste0("ID", hazard$dadID)
   hazard$dadID[hazard$dadID == "IDNA"] <- NA
diff --git a/tests/testthat/test-simulatePedigree.R b/tests/testthat/test-simulatePedigree.R
index ea4880af..a13ff5d1 100644
--- a/tests/testthat/test-simulatePedigree.R
+++ b/tests/testthat/test-simulatePedigree.R
@@ -20,7 +20,7 @@ test_that("simulated pedigree generates expected data structure", {
     if (isFALSE(beta) || (isTRUE(beta) && beta_match_base)) {
       expect_equal(length(results$ID), base_length, tolerance = strict_tolerance)
     } else {
-      expect_true(length(results$ID) >= base_length - base_length_tol * base_length && length(results$ID) <= base_length_tol * base_length + base_length,
+      expect_true(length(results$ID) >= base_length - base_length_tol && length(results$ID) <= base_length + base_length_tol,
         info = paste0("Beta=TRUE: Expected 45-70 individuals, got ", length(results$ID))
       )
     }
diff --git a/tests/testthat/test-tweakPedigree.R b/tests/testthat/test-tweakPedigree.R
index b0e63987..47625e7f 100644
--- a/tests/testthat/test-tweakPedigree.R
+++ b/tests/testthat/test-tweakPedigree.R
@@ -163,17 +163,24 @@ test_that("makeInbreeding - Inbred mates specified by generation and sibling", {
   marR <- .7
   gen_inbred <- 2
   type_inbred <- "sibling"
+  prefer_unmated <- c(TRUE, FALSE)
+
 
   ped <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR)
   #
-  result <- makeInbreeding(ped, gen_inbred = gen_inbred, type_inbred = type_inbred)
-  expect_equal(names(result), c("famID", "ID", "gen", "dadID", "momID", "spID", "sex"))
+  for (prefer in prefer_unmated) {
 
-  # do we have the same people?
-  expect_equal(result$ID, ped$ID)
+    result <- makeInbreeding(ped, gen_inbred = gen_inbred, type_inbred = type_inbred,
+      prefer_unmated = prefer,
+      verbose = TRUE)
+    expect_equal(names(result), c("famID", "ID", "gen", "dadID", "momID", "spID", "sex"))
 
-  # did we get more spID values than we started with?
-  expect_gt(sum(!is.na(result$spID)), sum(!is.na(ped$spID)))
+    # do we have the same people?
+    expect_equal(result$ID, ped$ID)
+
+    # did we get more spID values than we started with?
+    expect_gt(sum(!is.na(result$spID)), sum(!is.na(ped$spID)))
+  }
 })
 
 test_that("makeInbreeding - Inbred mates specified by generation and cousin", {
@@ -221,6 +228,9 @@ test_that("dropLink - Drop specified by ID", {
 
 
   # are the dataframes the same in both the undropped and dropepd relationships for all but the dropped ID?
+  expect_equal(colnames(result), c("famID", "ID", "gen", "dadID", "momID", "spID", "sex"))
+  names(ped) <- c("famID", "ID", "gen", "dadID", "momID", "spID", "sex")
+
   expect_equal(result[result$ID != ID_drop, ], ped[ped$ID != ID_drop, ])
 
   # are the families of the dropped ID in the original?
@@ -242,6 +252,9 @@ test_that("dropLink - Drop specified by ID", {
   ped <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR)
   result <- dropLink(ped, ID_drop = ID_drop)
 
+  expect_equal(colnames(result), c("famID", "ID", "gen", "dadID", "momID", "spID", "sex"))
+  names(ped) <- c("famID", "ID", "gen", "dadID", "momID", "spID", "sex")
+
   # are the dataframes the same in both the undropped and dropped relationships for all but the dropped ID?
   expect_equal(result[result$ID != ID_drop, ], ped[ped$ID != ID_drop, ])
 
@@ -262,7 +275,8 @@ test_that("dropLink - Drop specified by generation", {
 
   ped <- simulatePedigree(kpc = kpc, Ngen = Ngen, sexR = sexR, marR = marR)
   result <- dropLink(ped, gen_drop = gen_drop)
-
+  expect_equal(colnames(result), c("famID", "ID", "gen", "dadID", "momID", "spID", "sex"))
+  names(ped) <- c("famID", "ID", "gen", "dadID", "momID", "spID", "sex")
   # are the dataframes the same in both the undropped and dropped relationships for all but the dropped gen?
   expect_equal(result[result$gen != gen_drop, ], ped[ped$gen != gen_drop, ])
 
@@ -399,3 +413,55 @@ test_that("addPersonToPed works as expected with zygosity", {
 
   expect_equal(updated3$personID[4], max(ped$personID, na.rm = TRUE) + 2)
 })
+
+# Tests for single-ID specification (auto-find the other)
+
+test_that("makeTwins - specify only ID_twin1, auto-find twin2", {
+  ped <- data.frame(
+    famID = c(1, 1, 1, 1),
+    ID = c(1, 2, 3, 4),
+    gen = c(1, 1, 2, 2),
+    dadID = c(NA, NA, 1, 1),
+    momID = c(NA, NA, 2, 2),
+    spID = c(NA, NA, NA, NA),
+    sex = c("M", "F", "M", "F")
+  )
+  # Person 3 (M) and 4 (F) are siblings. With DZ zygosity, either could be auto-selected.
+  result <- makeTwins(ped, ID_twin1 = 3, zygosity = "DZ")
+  expect_equal(sum(!is.na(result$twinID)), 2)
+  # Twin1 should be person 3
+  expect_equal(result$twinID[result$ID == 3], 4)
+  expect_equal(result$twinID[result$ID == 4], 3)
+})
+
+test_that("makeTwins - specify only ID_twin2, auto-find twin1", {
+  ped <- data.frame(
+    famID = c(1, 1, 1, 1),
+    ID = c(1, 2, 3, 4),
+    gen = c(1, 1, 2, 2),
+    dadID = c(NA, NA, 1, 1),
+    momID = c(NA, NA, 2, 2),
+    spID = c(NA, NA, NA, NA),
+    sex = c("M", "F", "M", "F")
+  )
+  result <- makeTwins(ped, ID_twin2 = 4, zygosity = "DZ")
+  expect_equal(sum(!is.na(result$twinID)), 2)
+  expect_equal(result$twinID[result$ID == 4], 3)
+  expect_equal(result$twinID[result$ID == 3], 4)
+})
+
+test_that("makeInbreeding - specify only ID_mate1, auto-find mate2", {
+  ped <- data.frame(
+    famID = c(1, 1, 1, 1),
+    ID = c(1, 2, 3, 4),
+    gen = c(1, 1, 2, 2),
+    dadID = c(NA, NA, 1, 1),
+    momID = c(NA, NA, 2, 2),
+    spID = c(NA, NA, NA, NA),
+    sex = c("M", "F", "M", "F")
+  )
+  # Person 3 (M) should auto-find person 4 (F) as opposite-sex sibling
+  result <- makeInbreeding(ped, ID_mate1 = 3)
+  expect_equal(result$spID[result$ID == 3], 4)
+  expect_equal(result$spID[result$ID == 4], 3)
+})
diff --git a/vignettes/v5_ASOIAF.Rmd b/vignettes/v5_ASOIAF.Rmd
index d6c5a1ee..58c051f6 100644
--- a/vignettes/v5_ASOIAF.Rmd
+++ b/vignettes/v5_ASOIAF.Rmd
@@ -69,7 +69,8 @@ add <- ped2com(df_got,
   isChild_method = "partialparent",
   component = "additive",
   adjacency_method = "direct",
-  sparse = TRUE
+  sparse = TRUE,
+  mz_twins = TRUE
 )
 
 mt <- ped2com(df_got,
diff --git a/vignettes/v5_ASOIAF.html b/vignettes/v5_ASOIAF.html
index 73674c37..933c0ac9 100644
--- a/vignettes/v5_ASOIAF.html
+++ b/vignettes/v5_ASOIAF.html
@@ -48,8 +48,9 @@
 }
 </style>
 <style type="text/css" data-origin="pandoc">
+html { -webkit-text-size-adjust: 100%; }
 pre > code.sourceCode { white-space: pre; position: relative; }
-pre > code.sourceCode > span { line-height: 1.25; }
+pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
 pre > code.sourceCode > span:empty { height: 1.2em; }
 .sourceCode { overflow: visible; }
 code.sourceCode > span { color: inherit; text-decoration: inherit; }
@@ -60,7 +61,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
 { counter-reset: source-line 0; }
@@ -340,8 +341,8 @@ <h1 class="title toc-ignore">ASOIAF: How related are Jon and Danny?</h1>
 
 
 
-<div id="introduction" class="section level2">
-<h2>Introduction</h2>
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
 <p>Just how closely related are Jon Snow and Daenerys Targaryen?
 According to the lore of <em>A Song of Ice and Fire</em>, Daenerys is
 Jon’s paternal aunt. This would suggest a theoretical genetic
@@ -354,7 +355,6 @@ <h2>Introduction</h2>
 compute additive genetic and common nuclear relatedness. We’ll focus on
 Jon and Daenerys as a case study, but the methods generalize to any
 characters in the provided dataset.</p>
-</div>
 <div id="load-packages-and-data" class="section level2">
 <h2>Load Packages and Data</h2>
 <p>We begin by loading the required libraries and examining the
@@ -422,21 +422,64 @@ <h2>Compute Relatedness Matrices</h2>
 <span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>  <span class="at">isChild_method =</span> <span class="st">&quot;partialparent&quot;</span>,</span>
 <span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a>  <span class="at">component =</span> <span class="st">&quot;additive&quot;</span>,</span>
 <span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>  <span class="at">adjacency_method =</span> <span class="st">&quot;direct&quot;</span>,</span>
-<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>  <span class="at">sparse =</span> <span class="cn">TRUE</span></span>
-<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>)</span>
-<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a></span>
-<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>mt <span class="ot">&lt;-</span> <span class="fu">ped2com</span>(df_got,</span>
-<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>  <span class="at">isChild_method =</span> <span class="st">&quot;partialparent&quot;</span>,</span>
-<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a>  <span class="at">component =</span> <span class="st">&quot;mitochondrial&quot;</span>,</span>
-<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a>  <span class="at">adjacency_method =</span> <span class="st">&quot;direct&quot;</span>,</span>
-<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>  <span class="at">sparse =</span> <span class="cn">TRUE</span></span>
-<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>)</span>
-<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
-<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a>cn <span class="ot">&lt;-</span> <span class="fu">ped2cn</span>(df_got,</span>
-<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a>  <span class="at">isChild_method =</span> <span class="st">&quot;partialparent&quot;</span>,</span>
-<span id="cb5-17"><a href="#cb5-17" tabindex="-1"></a>  <span class="at">adjacency_method =</span> <span class="st">&quot;indexed&quot;</span>,</span>
-<span id="cb5-18"><a href="#cb5-18" tabindex="-1"></a>  <span class="at">sparse =</span> <span class="cn">TRUE</span></span>
-<span id="cb5-19"><a href="#cb5-19" tabindex="-1"></a>)</span></code></pre></div>
+<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>  <span class="at">sparse =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>  <span class="at">mz_twins =</span> <span class="cn">TRUE</span></span>
+<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>)</span>
+<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a></span>
+<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>mt <span class="ot">&lt;-</span> <span class="fu">ped2com</span>(df_got,</span>
+<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a>  <span class="at">isChild_method =</span> <span class="st">&quot;partialparent&quot;</span>,</span>
+<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a>  <span class="at">component =</span> <span class="st">&quot;mitochondrial&quot;</span>,</span>
+<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>  <span class="at">adjacency_method =</span> <span class="st">&quot;direct&quot;</span>,</span>
+<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>  <span class="at">sparse =</span> <span class="cn">TRUE</span></span>
+<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a>)</span>
+<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a></span>
+<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a>cn <span class="ot">&lt;-</span> <span class="fu">ped2cn</span>(df_got,</span>
+<span id="cb5-17"><a href="#cb5-17" tabindex="-1"></a>  <span class="at">isChild_method =</span> <span class="st">&quot;partialparent&quot;</span>,</span>
+<span id="cb5-18"><a href="#cb5-18" tabindex="-1"></a>  <span class="at">adjacency_method =</span> <span class="st">&quot;indexed&quot;</span>,</span>
+<span id="cb5-19"><a href="#cb5-19" tabindex="-1"></a>  <span class="at">sparse =</span> <span class="cn">TRUE</span></span>
+<span id="cb5-20"><a href="#cb5-20" tabindex="-1"></a>)</span></code></pre></div>
+<p>Each of these matrices is square, with dimensions equal to the number
+of individuals in the pedigree. The entries represent pairwise
+relatedness coefficients.</p>
+<p>To verify the matrices, we can plot their sparsity patterns:</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">3</span>))</span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="fu">ggRelatednessMatrix</span>(<span class="fu">as.matrix</span>(add),</span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>  <span class="at">config =</span> <span class="fu">list</span>(</span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>    <span class="at">matrix_color_palette =</span> <span class="fu">c</span>(<span class="st">&quot;white&quot;</span>, <span class="st">&quot;gold&quot;</span>, <span class="st">&quot;red&quot;</span>),</span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>    <span class="at">color_scale_midpoint =</span> <span class="fl">0.5</span>,</span>
+<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a>    <span class="at">matrix_cluster =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a>    <span class="at">plot_title =</span> <span class="st">&quot;Relatedness Matrix&quot;</span>,</span>
+<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a>    <span class="at">axis_x_label =</span> <span class="st">&quot;Individuals&quot;</span>,</span>
+<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a>    <span class="at">axis_y_label =</span> <span class="st">&quot;Individuals&quot;</span>,</span>
+<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>    <span class="at">label_include =</span> <span class="cn">FALSE</span></span>
+<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a>  )</span>
+<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a>)</span></code></pre></div>
+<p><img role="img" 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IhotTgDTbQ2fllsqQMsVThkbf3qQOfCNYVERESrwgCaaE1aPyQG0NKlwMViN7nddhGhZlTMEA6j/oaW6pbIyhSxpowA2pJxCP31luxTeokP/YiNcU0hERHRKkiapl24FxHlor7XG/hxVlQrX/5Y/MWXaqVPxbJ7bX1Mm2m3XHv6vqIPz4lvHWYuB7mQ32/dMpSIyBHOQBMVlvzedqh/sCy0zv6xbeeSz0DrE8/ZRaO1Jezv4jw0ERGRc1xESLQ2k5PmkDb5NoBWCS+YWhOnVMCaxZG03xjFUw/JBySN3by1BABIqeeSJes6HR/r6w7T/bUlSD7jrH4qPpV51BcXakuo2Yvbdjn5WImIiAicgSZaq6dmza/jLwJZk8cGeUd5bKSSG2vbEREROcQAmmhtTos7Ec6pUK4T9/eusyZAr6PzD9s0NvbhlrsYQxMREV0QA2iitZkUJ5vfjgA1luQoy9YqZYp1OYiIiC6EVTiI1maXJJ00Ncif154f8wYeM6dGBzVt1HJtmVThsGJdDnIBVuEgolXjDDTR2sxbWp7A5M8stTJeLr+NVFbAPVaIiIhyYwBNlL+RWzdLV9+tP99kOXse7b9dJ7Q1/r7tIsJEZluT7MMozZGKwrPr0GVq1WVJ178T+ust2adiY8YxczhzbfoxPQAwhiYiIsqJATRRnu6+Wtr3nVlE/05/eb2lqNw2wJsU2rwtJRhZoe1b7wEQERGVJQbQRHm4+2pJ+vLzAIDEG3rTdXb9nhQ3Ujnz1WIOq2hY246IiMiKATRRHqKnMs+N5OcWsWIdWjDyhJgDnfhhReVAZ2NdDiIiIjPuREiUh0AzcFbWs5PP603ftdSxOYf27s344rnstpr/ZJsDLRvbAdpLJYfomwhan6d56k0v03309uyzNXvFJ9l9hME09hlPDsL/SBPrchAREekYQBPly5hdNgLnSZse6iPnhBbPNrs7aUtYfNam3dsCb7OxETdSK/z0GDc5J4bLgLHsD6kION0/fgreZuOsfio2lrlKj6H1G+qPwn3iWfPt+xhDExERGRhAE+UhO4XDINbbAADl/TKeMWVxLNl+qUmy/epCPUSWijADbX07vbP+KNwn+2VtF/YzhiYiIgKYA02Ul0AzMpkVOkt6sNSKkX8Wc6DjfRWbA52N+dBEREQMoInW6j01QsO23WhvE+eJvR+0zYGuQIyhiYjI9RhAE+XLPLt88B3C6WQL1BfE7U68rUUdUqk03IWGu3DLXYyhiYjIzRhAE+XBJgc6ulloaF2CcpWY8py0LRdduRhDExGRizGAJsqDTQ50x4fMEXTdpT5AsQTQ3y3uwNYBczmIiMitWIWDKF/mFI7w180l6xa+F8bIN2PCNfEnVMCaBl3yOtDWt3NYhSNdNzqtsY/1oYmIyJ04A02UB5sUjr657FcysPRvgDUqvmajLCIUcB6aiIjchwE0UR5sUjj+17uzXyeA+BLauxqFCxs/bnu/BOKnbA59J5T0VLe2BG0p89wqOWccQn+9JftU9rtk3zDdP7tz+mVyDrExzBy2GWptF2NoIiJyG6ZwEK3Nn/9UrPl8Buor80Lb7CMq7qjwFA5rFgeA+BRqu3BLC3M5iIjIPTgDTZSvVMCsAoD6V5Yp4QQUy/aEDRumDrRAD6y9zTjYx3loIiJyCc5AE+Xh8RcyzxN3DqijPcov1ODbpiWD8k6oJ8So+txjKr5oiaG1JSw+a/M23hZ4m6EtGRPDek6FPh+cnBPniYFMPoY+wZzuHz8Fb7NxVj8VG8tcpc8o6zfUH4X7xLMyvr3NiI3Z7ASe7ult1nM5OA9NREQbHmegiZwLVy8BMKaX5dEeBVDfXhY6SZswKRbhQOKNDToDLWA+NBERuQBnoIkcU0+O1wELC6mXgILImJgCjXq0f7gGL5mCaO8uuxtKss2cLlKTzVIxcqAtb+cwB1q/1jr5nd2ztgsA9tt3ISIi2jA4A02Uh9hC1os7B1Tgji/uEPrs+A1ga1xo9AbsbqclEJ+yOfQcDK0YVTiy3iX7hheswqFfm90i9ESmVrQ/8SDnoYmIaANjAE2Uh5o6ZIpjRI6dUDHy92LK7ytHgBrxK6vqxuIPrqwwl4OIiDYuBtBEjik7t2e9MuaHT4mTzZgD3hTzOha+WrxhlSvG0EREtEFJmqat9xiIKsavN0oPnU29CPZHR3vwYW/g++Zw+RotekYKvG6+8h1R7RXLOsLT9xVpnGXkW4dZl4PKk9/vX+8hEFGl4gw0UR7msjOQIxNRQLlS7CPXANdblvpdUbgqHLExvH4Yrx9G/BRiY4iNiRnJZYX1oYmIaMNhFQ6iPNT7gAUvEAcga4MdAJ60VOFoxeQz4lI/+Z2FG4S3Bf4eAPDUG1U1bEtzlI/9XawPTUREGwlnoImcU+uqAZii1ZFTll6nR9q/KpTmaPytvyzmuMrf/q71HgEREVHBMIAmci761GkAqVJ2YQBotdanu7wVU8JOKjMP31u4UXibUdWKqlZ46uFthrcZks84ylPNXtTsZW07IiLaMBhAEzmmnpzMfnm4bUCFcsbSLaGo950T2qb/X7WYI6sQrMtBREQbAgNoojzUAEDDjXUygMQrkYloZj46YxaTM5bEaNkdW3lf0P4uxtBERFTpGEATOab0vKxpWvS/v7SUAIB5vAJgu6XbAtq7Nwtt3o+UYHwVgjE0ERFVOAbQRI6Fu6W2o7/X+cUX9L1T5I7f7gD2yjY9/2VeaNDEnA53YwxNG4E60CZZtQ1cKF9LHWi7YC81HM4r7SvcLUnd4XyuIKK1YABN5FjH4GPb//Se6IKRnyEPd9h22w51xtJ4vpgDq0SMoWlDCPZHtWzRfvQG1hrJqgNtgXtOFmaARFQUDKCJHFMHPnMiq7zGUo45pFngYklsjHERoQVjaNp4lJ6jIQwd51ww0QbHAJrIKfUr/zVq2iAl8l9DKuKWflPAgia0yddzEaEdxtC0wWWnedhNTJvTQNoGVKgDbYHeCCK9gXSeR66bhLvT153MvmHbQDhzRdYFOe5jHkPmRK52ImIATeRc5DtisBx/IoIPW3Kgb4ByvTgDXbeteOOqHA1H4L0Evl2m47a7/R2MoWmjCHd3DiF0QE/vUgfaAr17hlPJHeOd4l+twt2Z81q0PxjpvTes9IxG+4MI9ke10R4l903C3VLnuJFAcnSidyjrtpHee3B/6p5DnXrom+M+6kBb4NihaCYFZbxT6g7nbiciAAygiZxrsYZ59S2ApWLdOaiW7Qlnv8MUjtzuAGNoqlCR3oBpCWHnUGhYG9Tj5/C9vZHQsPECSs/RUKT33uwYVA0c0VKdAeXmQ0GMnxS+VeS6Sfj4EEJHe/Q/bXUc6Q9mX5Q+kbnnSoPZszP9FzKlZzQzpFztRMQAmsi5ektLLdSvWALon2NyVJyrTiSYwrGiO+D/CmNoqjyZRYTR/iAQ7I+mo0z15Dgw1JkdXMMcICuKgqxEjEBvxHL/XDdRT44juDu9Eapy86GsCDrrxIXuA6XnaCjVnj3DnKudiAAwgCbKg2bJ1vBi8i1Lt/No3yn2ZB3olewD9gGfhf+vGUNTxVJ6RodDEaEEh1ikQ8/KSAt3S1ImESNqnkZ2ehPHctynY1B/NZwKmI2PIFc7EYEBNJFz7T8bvMrUsOmWr7bjUktUfQlUS9Xn+KNM4cjtxdSxjzE0VbKOweEQUjnHUHbuQeTYiRW+9MPHh9KpzgCiE9YZ6Fw3UXbuQWQimn5td62T+5hGr4fLYhGRXO1ErsYAmsixez//vOm1r6kVuERcLyjtAHxio7yrcCkcsTEsT2J5ErExxE+ZjvKUnIO2hPgpeHfAuwOeevHY22ccF/XhUB/rclDl6jjSH0yH0B1H+oOR3jtTCwfD3Ta7rKSDWnWgrTO1EDA7Os51k44DWbF6uLtzCCvLdR9hB5Z0akiudiICwACayLkH/kxI15j5xh02Fes8VwJ7xHTpqkuKObINZh8YQ1OlUnru7w9iSC9xofSMRvuRWmbYOd4fNSdfdAwOh1KrEAPHDkWHQ0bcrEfHegSb6yYdg5nkint294cuPDLb+3QMasPI5EYHjh1auZ2IAACSpok//onI1shN1fseNRWC9v2S9rO4N/B90zrCxj/QnviBNxAWG898wXLH0/etZhyxMch+AEhMw9tiOuVtXs0Niy05B8mHxBmnw9On0r/zx9O3v1nUcZHL+f3+9R4CEVUqzkATOdXqE1u2fxjYUy00bn0XlI/UCY2bbyjcOGr2IjGNxDTOPwxvM7zNiE8ZR3ny1EPy5RHc6x8L91ghIqJy5V3vARBVjMnXxYp1s2eg/M4fBL/9u5FT6S2+g/+tA3h8Ueg5Nwq0F24oNXszj9lPylxsDIA4aw7LxHn6wzlYIR8XERG5DGegiZxqH4s/dsumzOsaHPplAN97KhM9A3j2X4EHvrYkXPvWN1mFYzX8002chyYionLDGWgix9SBTz82n3mpb5by+IsxU6eFZ0bwCesXliX9w41WMVPe2IeD8D/SxHxoIiIqHwygiZxSf+/31Nms13H860kVSzNCtxjQfq2M06Z8D+8HuXw9dwqHpz7ny9gYvC16PjRjaCIiKhMMoIkck8Uc6K0ATgu17RKxKeA/1CB7rhqo/VBxh1YZVjEDrV8Sn8JtdzOGJiKiMsEAmsipyI/FpYE/+npkZDEuNL5w74haHxMal8u1QkZJzRxGbZdNu2+X6aV1Btq3C4mznIcmIqIywQCayKngxy7F77+U3fK+TwZbT27CSdNks9zUqlxfhR+apqvrLWkLbtTYl/cl+gx0bAy+XUic5jw0lRFJ3HB0NbgVA1FlYgBN5NjMrLVN+Z2r5b/4cXawXHOdgrPitPT508UcmKvst5vDJio9cb9RInIRBtBETk1GzwotP3pwUv3uT4TM6JiqjkTFAHr+m8CRIo5tg/O2QPKlH/3TTXgMnIemdcYAmsjFGEATOdU6KS4iTMy1CtU2AOAytC7IOGluv6iYI6sUyVkAkMS9GxF72vSy1rJto7cZ2lLmUa9t91DT9CcYQ9P6qVnvARDR+mEATeTUpLiGEDg7OVkltiWemMSyGFXLm8RutFY3MoamdcXi7kQuxp0IiRyz/sXWi9b/IAttcqDV5tpz3ImwCG6E/yHuU0jrpKoQBxFVJgbQRI6ds7TE7L+GlM/VCS3yB7iRCiBVQ6qGJItH1TtMh/21cuZRt+l2SMCB2/1/zRia1gMDaCIXYwoHkVPttzZgwFSIQ76iXZkSszUaPqXgmJjtkTxT3LFVBinH37y9lzi4WM56BADU3mBkSx+6gbXtaB0w/CVyMc5AEzn2ynmxRQOWxBQOABgVo2qN6ZJFtb/L/wjnoam0vIU4iKgyMYAmcuqBfxaL0yV+DtSKsfLyFNQqS1Q9UbxxEQDG0FRyDKCJXIxfvkROtVh/39wE1IptsVEo7RLMldmQI7OXCuP8wwCwD8zloNLhz08iF+M3ACKnWluAt8xNjcAlMmCehJ4BXhK355W4lTeA5Bxglwn91O/iSgBAzccRn0TDbTbXakuQfMajLv6a8cTTgIa7ACA+hf3w/8+m6c8yhqbis8veIiKXYABN5FTkFUvTaajLlsYXgP+rAf9o2raw9rrijatyeHJs3ba3L+vFe+z76HFzdvCdvfTQ0wAA3mYA+PRe/4NN07/OGJqKjAE0kYsxB5oo3C1J3WHjiSRJxiuLFuvvmzVQrrE0BgBvo9DGX1VL6kb4H2Q+NBWZXIiDiCoTA2hyu3B351BoeLAD6sA9QwgNa1q0f7zTNoS+2NKyGbhZ3M9X2gT16y8LjbPfLdR4K1lyDsk5aEviMXYYM4cxcxjnf4zZv7O/VlvKPOrirxlH0lRbELPfhge45Xb/nzOGpmJiAE3kYpwXI5cLHx9CaLgDUE8ciyB0tAPAzj3oPR4e7OgQ+p61XK0Br9YB89lttfXAWUsZu3lQEVM4sjXcCtwKAJ9hfWgqJv78JHIxzkAT6aITEQR3B1boMWUNgk8DX/r4JnObrwXK5zYLHauvWvMAybnzT2JK5M4AACAASURBVOCtw4i/hv1d3KeQisVTiIOIKhO/fMnlAruDGD+pInx8CMFDNyswJqUPiNPPQIvtxmN3f2/e/HfYuvMYuUfc9Xvhn9VCjZjysw+MoakomMJB5GIMoMnllJ77+9EbkDqHEDraoyDcLXWO90cHrfEzYN1N0At86cilWfkaMuStl6P1HeIPRt+lBR015YUxNBUDA2giF2MOF7me0jOq9aRfdQxqYg3nlKlZS1MS+NRnsqvbJeTEB+xCtWS1sqZBUl5qb0DtDQAQfx2b7wKMpGiiQmL4S+RinIEmcirYbGnaCjWWNLUk8I2/BaKWRYRFHBflVrMX3mb98Cce5F7fVEicgSZyMQbQ5E7pks+52NSxm1y0NGmY/Ne40Bb/F2CX5Qcjf1KWg31gDE0Fw0WERC7GL19yp45BbWV2SdCWHGh5C9q7xXlpuRlKuxgvy2JZDlonjKGpUDgDTeRizIEmcmrKUlA48RpGBk+JjW9C/aGYwpEQd1ah9dDYBwAHwfrQVAAMf4lcjDPQ5HrqQJuzFI4W689LD9q7twtt8jVQYmJH6fqCjZcK4GAf56FprZjCQeRi/PIll1MH7uxFf1TTov1BhIY1TRsOAaFhuxQOuz/YqH89LbRIVcAeSWjkV1rZYS4HrVEJAmh1oM3ul/nsX/vtThNR0fHHOrlcdCKCPTsVQNm5B+MnVaBjcDg0dM+AdeOTqSVLUwzKfR8V4uode6A+JlbdSIwWcMy0WjOHERtDbAwAaruwb73HQxWt2AF0uFsK9EZsTwSOHYpqmqZp0f7xzjab71ZEVGQMoIkMgd3ByETUeJF5ltFSY2mqwUjP3wtlOF77HpTbN4k92woyRiow/3QT56FplYq5iDDcLUmd4/3D/UGbc8eHQkd79MrySs/RkN13KyIqMgbQ5HIdB0IYOh6GPgU9dDwMqCfHEdwdsHS17i+IRps7Js6p6ui82PpDzhGVK+Zy0Orcp2UO5xFz9lX35SwQ3zGoadpoj/XbkH4uk2IWPj5k992KiIqMVTjI7ToGo/1tgbaB6GjP4HBI6pSGgGB/tMe6c+Dka2JtDSwCC2KjXAulrQEnzmY3bvpN7kRYBvQqHDpvC7bcDckHAB9jXQ7K35GspQ7iqgdnVwG4dy2bLKkDbZ3j/dFBfnchKjkG0ESZvbw7BjVtMGe/qQVL01m7PQZ9k+qrlq5PAXesZZBUZPsYQ1Oe1rmMXbhb6hzvj47a/LJPREXHAJrIqRbrl4sPU5Y0qMTPpybfFNcbnvs/KsCfc+XEa94Bp7GP9aEpP+uYAqkOtAV69wxrozbVgoioFJgDTS6Xa09vu9pQlp0IUYWWqy1LC2uCrZY2uY3RcyVgfWhybr3qQKeiZ7tam0RUIgygyeWse3oPhxDsj9r8bLJJ4UgAc8tiY42idIgRtPMMSVpnjKHJoRJv5Z2qCa2eOBYBhjpX/nWfiIpL0rS1rGAg2ojC3dI9u62phSN7pH0/Mzddo0Uv8wbCpnWEclD73pvSPqGw1K6o9rxlEvr0fYUZMMVPAUB8CgC8LWJ6xip86zBzOTY8v9+/puu/Uojfi7/CH8FEFYkz0ER2bCurvmFpOYfJJ8QqHIkX0f75HUJj3Z1M4SgmbzO8zajZi5q9BYiewXlocqDEM9BEVE4YQBOJwseHbOtA23y5JPHoWUvjzIj6wOtCW/ytAg2ObOn7C8bGMHPYmI1eo/gp7O9iDE0rWa8caCIqA/zyJZezWUTYORTsv9+mNJTNVt7z+JRl00Hvr7TjDXFmKfFi4YZMJcM9VmgFDKCJXIxl7MjlVi79bGJTxk6GUrUMQAbSmRyX3ovJ/yR2TPxoBGhf/TBpZd4WAIhPobarYPes2QtvC25hbTvKgeEvkYvxGwCRY9YAugH4xDZkRc8ApAjaLdPSciuj54rFeWiyxRloIhfjly+5U67yzysVhmq1BFHyNmAqJjTOnIbad07sWq0WbvCVKwEkgKR4JM+ZDnvJrEcAwPLLxpE8Z1pEKDcaHbTF1Y9UX4mo37axDwf7/A8xhiYzLiIkcjEG0OROmfLP0f4gEBo21YFGaNimDnTkFbElcQo4LUZpSwCuFn8wSo2swgEkF5BcgBYXj8WfmQ5bWjzzqHur3ziES+LT4pOC+NUD/kHG0JRFKsRBRJWJOdDkcuF7eyOh4ewNcTsGh0NSZ/cByz5fLZuBGVOLfDGwXbzjsgrl+mr8s2nblWqboh7uI/kAQLJMu+kZzBe4Vhav3XzA/nK5QXxSEOeO4yb4B5umu5kPTQA4AUXkagygiRyzxGNVV9h1a4T6alxo07YUZUQVRtK/4VjiDvkiBxd7xGt9O1Nn6s3vUiM+KQj/XYhP4RNcU0gpTMAgcjEG0ORyHUf6gwHTfHO4u3Mo2B+12cr7ZbEl9iIwIaZwJLZBubEOD5uK3iVfKtSAK1qOiEOIgO1JWY8AkHPDFH2eO/tJQej50AAO7i3kbalyMQGDyMUYQJPbKT2j2s5uScr8MAwNa6PW8BloaQCsK9yeFncixFNQ//es0BZ7cq3j3BD0JYCWuEMzV9iWqu2u1QAp9ajf7Fymv+mSRCpSTxRykjB5DomzACBf5F/4NP73303/Gueh3Y0z0EQuxgCayHExaLE2HQDgvGyuYgfM2sTZsu21bqMHypLl287yq6aXvnfaXZwAvKlHAMDCD1L9rzJdkjhnFOJIPymIhR9gcgQArumDtxkHPuv/VNP01xhDuxhzoIlcjAE0kWOa2CD77X6INmDytGVa+uIijamy6FN2lhloj5PVftYUjsvsLy9WCsdleEcqTNff8VYwhnY1BtBELsZvAORO4W6j1HOugtA2daDRYNmgex7i9DOA59F6vfirqbTIOtCA5IFkt5mEp8502LMsIvQ2GYdwiVQlPikIbxOq34/q9wMwxnkD8IfcY8XFuJEKkYtJmmaZVSMiW99olD55NrvBu09b/qkkmacg5Wu0+J310l3z2Y2XPqa9bN2L8PR9xRhmGcvx3UZbNr20nzm25kDPZfqbLilSDvSc8Y7eZiMXZfFZADj/MB4D63JUIr/fv6br/7IQqwj/M38EE1Uk/v5L5JT6+2eFlvjrwEcteVBtGPkbcXvCVz4/UryB0TrjXt/uxJ0IiVyMATS5XLhbktoGVptfoQEfEktGXHoErQGx0XuRdf6ZNpB96z0AKj2mcBC5GL98yeU6jvQHI72B3InPGUrS0rQABN8hzCL5AMVSodh34xrHSeXOP93EeWh34VbeRC7GKhzkdkrPqNYDAOFuqbNTGgKAYH90tEexdD0vNsj1QORtYRXh618DZsWdCL2WHb9dKUe84KhchqUKR87tV2TLk0Lw1GfeUR9wzd7Mo+4g9yl0EyZgELkYZ6CJDB2Dmm44FOkN2E1Gf85SzDkIjIrR0uwJqG+IpTkWHi/cQCtYAkgASfFInjMd9pJZjwCA5ZeNQ7hEWxSfFMTyy1j4Pha+j/gpJM/hrcOYO4HYs4ifMtYX6sfBPv9DnId2h+LOQKcKBNlmmKkDbSvUCyKi4mMATWRIF7TrHAoNZ3b2znK9WGGtRsEDD1vK2J2E8jGxgJpXXH/oTvqyKWsZu3rTYc9axm6HcQiXFKsO9A7UvAc174G3GZ56bPsT1Nxg7O8tDP72P2IuhysUMQdaHWjrxLCmaVr00DHLr/Ph7sCxQ1FN07Ro/3jn6tdwENHqMYAmt8uKm4P9UX0O2i56BtAq/snWtx133GKZlr4CD/yWWIUj9hircADQclSy08zHKm5bGunh6U+SphnxbPu7GENvfEWswhGdiIQOdACAcvOh4NBxUwStnhwPHrpZAQCl52goMhEt7sdJRDYYQJPLhY8PITSsx812ec/ZIvNCw/wzwGkxT8D3HxG0lpe9lFU4ctOSpsPpVcupQ7hEszwpBG0ZyQUkF4DUgBNvImGX7qwt4/zDrG238d2qZQ7nORvZV92a47+oenI8uDugP1d27sH4yexJZmXnnsixEyoAqAP3DKV7ElEJcREhuVzHoOO9hNQHFoSWpWPA4BV4NHsCSH5PB5Rtm/FFU2KuvGtNo9xYrJ9wJ/8Elo1UcgbKxQmgjSnnrDtr4lLRzNnaLrz2MPbB/z+apr/ANYUb1Hezl7Su6ioAv2r3vzQ6EcHu3LfoGNTQLUkSci54JqJi4ww0udNqtvKe/Kkl3XkWCNxgzozefbQdgDhXLfFLDcidoSEsK3R4syXjEC8p0gz0UmYzQn2c8VcRf9W+p1SN5o9C3otfg/8I56E3qOLlQAd2B1d4X3WgTbpnt55vdnQiwHWEROuBM9DkTumJ545BTRt0dk379TL+0RxDXwRERszz0u8AgL+0hNqWTGlXyrWV95LppVST49pcW3nLpku0BCSv6UlBJOcQfw0AfO80BjzzT4C+lrEl083bjOQc5IshN8GzDU3X4OgrnIfemIr3W7Gyc0/keBRQAKgnx7HnQPYkc3QiEjx0v97ScSCEe06q6OAsNFFpcVqMyLE5S8sWjNx7ytwU/th/xshmcXFQ/AdcKI+caaGS13TkvBamul+eOuMQL/FYnhSCpw5yE+QmAMY4t7wXW96Lmr1GLQ790Hsm30ZiBstRLP0M8Z/i0+/x/xbnoTecIlbhCOwODt0zoAJQTxxLrSfMOpnKgUb4+BD27GT0TFRyDKDJnXJlbqyUwoFfvkRokBttJlUXnxlp3SzWd5Wb+SNuBY4KE4ikKuMQLkmnyxQ2b0aqyqqyJwMyvJfBe1mOng2QN6OqFVUKqtvhvQy/Af/XGENvLEWsA630jA7v6Q1IkhQ4diiqlwVSB9r070uZk5LUOd4fzVE1iIiKiSkc5E6ZzA11oC3Quyer8HO4W+rEsN3PpDNiMWdpK2BJgk3Mt2KnGFZLH1j7mDeAXPHC6nY0TsfHpdkQ2ZM11S0BgKc2Z8/4FLQEYo9AfhfghW8X3nktdu3yH2mavpe5HBtFcXcitGSXKT2jWs6TRFRinIEmlwvf2xsJmcLljsHh0FCn3Qz0c2IVDm0WrZb9vbEJSou4kYqPW3kTgP3wf4Pz0BtFcXciJKKyxgCayLFpcWlg4nWgxdItBkwti425Kp6Rq1wJ3MQYeqMoYg40EZU7fvmSy3Uc6Q+a55vD3Z1Dwf4jNikcfvFPtvISsM3SuAUjY2KovTBcgLFWvsKWsUttpFKiMnbLSM4jqRco1MvYvWbU5bD2TJt4Gm/9GPHXMPMs4i8ajTextt2GwACayMX45Utup/SMasPozCwf7MRwjj0Jf1FMzMAmKP2KEEFLtyitO8Wo2suNVICcAbSWMB1Ob5beidB8SXpnHMdb5Dh9u8xOhAloCSQmkZi075l2JRADEpOYMffZzxi68jGAJnIxLiIkcrwi56RlcrQJ+F8/FyI+z2vAlBgFaudAKda41knQbKkDnakeLdSNTlqeFIK2hGT6XzEBAHM/B4Atdj2zvQBsfsHmhlfDv7dpeoxrCisWw18iF2MATeSYJQhOvAn1NfGnaOKnqhKQ8bQpKJTeVdSRVRbryikna6ksdaAzzyW7ng5v65wESc48xwpFGCRI1UACnqvgO4vWU5gFzgOzY6irNrpsXkQzcEdBB0glxgCayMX4DYBcTx1oc1gH+lKxQV6EUiP+FrrjNxQ0ij21sYKNt5LlqDuQruhs1HV2JrORivmSItWBNm2kUgWpCvW/hPpfsu+pLUJbRPJ5LJ3CNuAdH8ElQMNexBaN4xxQD7wb/qebuKawUrEKB5GLcQaaXE4duLM3EsoqA70Cy9LARA3UFrG+xhthFUuWnAT/6oe4geSIaKVq+3Zzp6xHAIC8xXwqzWt5UgjyltQuKqkB19lFz3rPcw+j4S5IXWhoAWR46lDbBsmHunajTyZmlnF1wv9I0/TtzOWoNMWtA01EZY0BNLlcdCKC0FFnO3lZ9s2Qq6GcFGNlT6uCN71C4bqqy1c9wo1E/1xZwuikucC2Z5PdtZYc6Ph0pn/2JdqyMSedflIQ8WnETwNAzbXGgGe/CQDV74c3q5ahtxnxaWy5B9DgvQxIInkOsR+iKgBPfWYrluQ8kgtInoMkQ27Cx/7I/0DT9B2MoSsK54+JXIwpHORygd1Bx31nLPPKS1CjYlvsuKpOiOvk4j/Jf2jkKrfc5n+AuRwVhVU4iFyMX77kckrP0Rz7DlpdZvmTrd9SAQKQGxTlfeIcqufKVY5vY8mVA+01HQ55aoxDuKRYOdA1kLdC3gqkBlx1Haqug7cFnvrMofc0Ko2kHt/6Z8vtNPMBeJtx8LP+bzKGrhwMoIlcjF++5HLh7s4hYKjTySLCrWKqbuJNKF9qFhrrDgH7xZ4NgQKOmTauG8EYumIwgCZyMeZAk8s5LAENAGhoAEzZunI10HF3HT6Z3Vq9HfirM8Kl85ZMDyJ7N673AMghhr9ELsZvAETOLYkNDUD4S+YVcKhaBiQxUWH5qSIOizYY/1QT56ErAMvYEbkYZ6CJHHt7XmzRgMdOC201pzEyIta2SzytAnbbg7uLdTMUvcHJNyLLtenid+LlxdlIRarOFM7T31EvreKph+QTe8IDaEZtEMmHpl+F1w/Jl5mzkKrh2QztIkCCpw6SB/JFALDloNHhY/D/VdP0x1mXo4xxAorIxRhAkzuFu6XOoZU62JWGfsYyA/06cKlYse4s0Gq5nfweRs9plq28NfPvG5Jtfd0VtvIWLtEsTwpBW0JiFgA8m40Bx18FgCrL/jraEpILgIb4G0AC8ZcR/zniU/BeBk+qFGJ8ColJzL0IGaj/RdS1G6XxpLrMfW77vH+gabqHMXS58jCCJnIvBtDkTvmkPqddXoOnYtkNiRiApNBr9mkov1iDb5p6Vu3Of4xEvwzG0GWs/sJdiGiDYgBN5Nhmy8yoD0gkATm1RQgAaK8D/yWJb5o6bvl00UdXCXLkVKxuJ0JPQ46esuVJIXgaMu+oD7j2Axfo6dUrtLwb8VOp52l7AWBL9lWbgOztFQHJh0u78Clwn8Iylf3nAiJyGQbQRI6dXRYa5KuBDzXifnNw0wD178QtV2a+ouIhZnHoORXWMFr4dDkLfLVF44nkNV+STvPQCpkGrS0aSSOeBmPAy6cAoKrFrmcc0IxEjuQskvNInIWnPpNqIuxEWHWZ8eFkb8rokZA4AwC33MV9CsuSk1/8iGhjYgoXkWPWL5dZqF8VK9YlJjH5ghhALz1etFGRG7C2XRmSfAU4cgp36yXp2wbUnH3UgTbbkvVEVHQMoIkcu9TyF9u37Lot2ywixGZOP9Ma1N/qTzzIvb7LjK8Qhy11oK0Tw5qmadFDxwI5YmR14M7eSPE+OiJaCVM4iBx7ZU5siQEL4mQzqqC0yBg3tcvXFHNgFSNXQsWqkpVzZk4XrYxd5h1lwC55Q+ip7+yN7c7u7wMsid2bUjPPegp1Vx/zocuI823n8xadiIQOdACAcvOhYO/x8GCHWBRIHbjz2J5QkBE00fpgAE3kmFhvA9gCXOzFi6YqbN6roD5viaovKeK4yEUOMoYuG4GnM8+jNzi+6okL91FPjgd3H9GfKzv34NhJFR2mP2KpA3ceO3T//bhzaMLpGxNRITGAJnLMkvGUWMTk2+KeKdoclPfKmDDH0K8Vc2AVTyjY7HDmOP0Z9pgvKc4iQiSgJQB9qlgDgOQcYFsMJIHE20jMQPJiSYX3YlS1IhmDpxrJVHHDxLRxoeSFFoe3KTUNn/WpkLxIzgN65elFSNXG4z74H26a7mIMvd7UrDIs9sXLL3QVAOXfbPpEJyJYqfKlHj6PKhhw+q5EVGAMoIkcu7Ra2DNFTqJVlsUiErM29WHly4o8tsq2ujA3V8hSnBQOyFlBkgSsWEdPvsjYWbAqlQ/v2QwAnlSVOq+zvA79KqTyVfTHxj7cztp25aBoP0ADu4MrnE2Hz8i9vJCIioyLCIkcey4mtngwafkZmjgHVZyVtsn+IFqTffA/wjWF60ryFuCwpezcE5mI6s/Vk+PYszMrf0M9cSwS6Q1IkiQFeiMY6lyxUAcRFQcDaCLH5iyZzZejdYc4D+rdgcnnxJ7aj4o3LHKlxj49H3q9x+Fm3kIctgK7g0P3DKjQw2VjPaFB6RnVDNH+IELD2mgPi/wQlRpTOIgcO2dpeRmT80tCm7aA9g95ETXNQkvvK+bAKoaWo9m8Q419cVw9oTkrrTk5l+lvuiSRyu5IFHIzwuQckrMA4N1h7KgSewYAat9j13MOSCL+OrQ4EmcgNyH2bdT9OhIzRp/4i1gcAwAPULUX9b9q3DybpwGLzwFA7XsRfx3eHcYjgOTbRjbI/i5/qGl6iLkc66GIVTiUntHhCSkg9QLB/uhoBwCoA22BiaPaoFiOg4jWAwNoIsc+XoMvmrI4Egm0f6IRX5zJbtz6AaijlhyOl4s9OHKrz8H/Z03Tv80YuvSK+gO0Y1DTBrMblJ5R8TdQmyYiKg2mcBA55xdey4D6LXFe+uyjqvKZRqGx+l1FHBa52gvATfD/GXM5Sk7yFOAgosrEGWgix06+IbZsReRFMd05cRrqN2eExsUnizesCpKjLMZKGxoL12bdwWOpdWKQLU8KwVOfeUd9wNbkDaGnN6v6t9g5CHSJV1nVvtd4omdu6I/IquZx/mH80l0AEGJdjtLjD1Ai9+LXP5Fj5yyLCJdwx0e8d5o3UvHdriiveIWCd1ipKhVVSh3oJABIVak60AsA4NlkNzA5k7Us+SDVQItBqrZJdIYMJODdISaCA5A8Rt1ozyZoy5CqjEcA2pIRxDf2ZXLBD/b5/7pp+tcYQ5eK89rPRLThMIAmcsyyk3fiDB54REx3XvqHB6CIiYk+S1K0K+mfFmtQK/xm4uz7UjrilIRqBsUJoLVlI5yVtxoDTrwBAJ4rbHpKMuRtAJA4Cy0GeSuWX4fXj8Vnxc6eBiRn0XCbsWeK6VRN5i2S85C3Go8AEAdS0/bx1CY9vp3Y/3HG0CXEAJrIvRhAEzlmCaCxhJaLgZdMbXJjywOTYsfFvx3BF9qLNrJKt8aNVITLi7eRSpXpzpJ17jlrYPoksacWWg2kTfD64WmAt1XsK1XDcxGArJtnv2PqLfSzmT5ZubPZ+7ks/BXa4X+oafoTjKGLr4hVOIio3HEFA5FjO+uEBvlitF4r9kpUtQbbq4XGKz7L6BmAVOigtowZid1eSCt+yKv8fOS4rO7jkICb4P9rriksAU8hDiKqSPwFmsg5y36Cm6D8ghf/YN7fe0lRFsXi0OKiQjJZVRiRqWBQkihE8gDmCWCb7Od0z3THTYBeqdoDqRpVOyy9vZDjgN0MtOTJvIUwA52dfWtafRhH7SGcO4b9h/xDTdMhzkMXE2egiVyMX/9Ejk0tii0ewFJeI6FB/Xsx5fmt+4HfLNrAKt7qpmHTcWppUjg8WXPJegpHruIhHiAV43rqAA2QIXkAOVM9w9RZX5to/W4sZd5CP5vpkxWjS1l/7qi6HAC2dMHbgltZl6PYOH9M5F4MoIkcs8Zj8xgZtywPfBu4CBDiltMqwO12cxGm9p3FJVrqMy/J5kuKtIgwbixb9NQZA06cBQCvJVlCi0OqMjKhEzPQFiHVIXkWngbEp8TOUjW0RdTsNVYomk5VZd4iGYOnzng03iIVWyfOGE+8zfA2Y+YwGvuMx4Nr/7ApN85AE7kYv/6JHLukFjCXITsPLFi6ycBOGf9uqiwhNzF6XsHqUjjS375KMgMtebMSJzwA4BW3yzEPLAl4IF8EaJBkaA2ADM9mS2cJmgYAnhrrjTJvoZ9N98nO99DLfejib6LhC1gcR8MX8PYQtoT8iQfx1cPTn+U8dDGwCgeRe/EvUERr0r5P/CEqB22SpdGqlmY8RKIA/J/hmsJi4CJCIvfiDDSRY1PnxRY/cKUslDGWJNjMfb7MGegNINdU9wpXSND02iN6CrV1zlKClKs8Niyz6XZ9sqt8eHwAoDXA44M3YDReCXyO+dBFwI1UiFyMATSRYw2W/QXngVGx4Ia2HcoWn9jTsokhrVmJK+JZ3y5X/KT31CcXq1KhrwysnDVr++EIta5ts1Oy7qkvUtQfN33EaLyiDwCuYAxdcAygidyLfz8icuxSSwmF8xg5a/khOgq8YqnX0VKsQRE5dfPH/A8yl6NwJLkABxFVJgbQRI5NvCW21AHilimQLsbIqDjhnHhN7EZUaot/ixvhf4QxdIEwgCZyMaZwEDnWYqnCUYfWy2SMmcJlzxVo39uI/2baO6VmVwnGR7Si2i4A2L/ew9g4OANF5F4MoInWYB44I042J18GcE5orLqqRCMiWom8HYnT/ukmPAbmQ68V54+JXIy/QBOtQRLKLzSIjf8R6tNi28LzpRkQkTP7mMuxdnIhDiKqSAygiZxSxy1l7BZUfGar0PaOJkyetRTdeKVowyJyyLcLZ/vg24Utd2PrH+Jjf8gYem0YQBO5FwNoIqeUu38TPlNL1TsVPC7EyvIlPrRutfxc9HEjFVpvnno09cFTD089EmeQOIODfYyhV6+4iwjD3ZIkSZLUNmD3vWPls0RUdAygiRz73neRMP3Ai88Bp+fMnRI/fRR41TIDXceNVKgs7e9iDL1axduJUB1o68Swpmla9NCxQHc4n7NEVAoMoImcUn/yFhKmyFh7TcU/zML8h9jYD6BU8S+zVDkYQ69OEWegoxOR0IEOAFBuPhQcOh7O4ywRlQKrcBA5dl4TGqouA9oa8MOZ7LC67gDUnwLj5q6vF3twRGuwv4v7FOZt++eKdWf15Hhw9xH9ubJzD46dVNGhODtLRCXBGWgip2yWBi5N4qC4PeG27VCq6oRGSWwgWld6JjQAb4tx3HIX56HLRXQisuqzRFQSDKCJnJqaE1uWT03hc68KjW8+CmhivQ59Cwuisnbb3Yyhy0Jgd3DVZ4moJBhAEznVsk1MWJSVFtzmF2pRadcBv75D6Li9pciDI8qL5IPkA1JT0foh+VgfqXzR5AAAGjtJREFUuiwoO/dEJqL6c/XkOPbsVByfJaKSYABN5NiSJYUDwOlpwNTefAtw7LS5U+KtJ4o4LiJHYmOIn0JsDLExo4wdLAF0Yx9r25WBwO7g0D0DKgD1xLHUikGHZ4moFBhAE61eEhj5m7jQOPl5PPDtJaFx5v9jtVaqHJyHXmdKz+jwnt6AJEmBY4eigx0AoA60SXrJOruzRFRakqaJhQWIyNYDV3nv/HfzJPSV92s3fFJ6xBRD192vzU/US380n93o/aS2/DXLHU/fV4xxEtmLn4KnHsk5AMYKQv3RKjYGAP/w8Mauy+H3+9d7CERUqTgDTeTUHf/zfV7zVoR1H7oDUXEGenEU6nhMvHixqEMjcsDbDE89vM3Gk1zRM4CavajZy1wOIqJcGEATOfbQs3E03HxzpmFRxQPPi70SJ4AFsVFTmcJB6y05B20JyTnjiSYmGmXo2dLJOcbQRES2GEATObalDnjrxImslvO4Y7+lNEcPcF5cbpioLfLYiIqE+dBERBYMoIkcm5wVW+oA6yze41A8lqjaz0JTtN70OhvpghuSL2fPmr1GmoeOMTQRkRm38iZy7N3bcNyUnCE3A+cs3RqgQhIbp1WAMTRVoMY+ADi43sMgIionnIEmcqxKnFe27NgNAJiFcmir0FZ9I6Nnqmz+xIOchyYi0jGAJnJqZPAloSX24xFYy3wlgO/OCG1x/rGHyt/M4Qt04JpCIiIADKCJ8lBvmYG+BLjW2g0IiCkciR8WbVREhaJna6yMMTQREQNoojzUiA1V9cA2MVaWtmDkm2Jx6MSTLGNHGwVjaCJyPQbQRE5NzYjF6RbUKZwRu/nq0brdcvHVzIGmDYQxNBG5GwNoIqdaLhETmeuUFnziI8LE9ObroViSPbyXFnNkRCUWP8UYmojcjAE00do8JW7bLTcB3TuEXr5fK9mAiEplf9d6j4CIaH0wgCZy7LwmNCQB9YRYhuP0CPDMG0Kj90dFHBdRqcWnAMDb4p/v5Tw0EbkQA2gip6w50DF1CtfUi/3egvp9S88JLiKkDeT8w5nn+7sYQxOR2zCAJnKqpVHMbK5TWjA5JzRqUeC9Ys/qa7iIkDYQveCdtxk1e1Gzl/nQROQ2DKCJVi8JTP67pWLdq1Aut3xlnS7RkDam5ByQQGwMsTEk55Ccg7a03mMiM8bQROQmDKCJVm9xDjhvaV3CyF8ICwsx+3dM4VgDTz0gG5Odnnp46iH51ntMZMEYmohcgwE0kWO1Nm1T85amebVVjJ+BzUzhWAN9Bjr9qE9FU9lJYB8YQxORGzCAJlqDegSvENOd4VdwsaXnZs5AkzswhiYiFxA3hiCinM6LtTVqAeUKCT8xNcrboezy4hlTbnTNrZyBXgNPvemxZu8F+i9FAUDeBvkiJFJ7RcoXFW+ABACQjcWFB+F/pGn6drHCIxHRhsEZaCKnpl4TW2Z/OoU5sTg0dgAfaRDaaq4v2rCIyhDnoYloQ2MATeRUyzst2RqJFlUWp6Wr3wP1R7NC48JfFG9cRGWJMTQRbVwMoImcav8vl4lNm9pxRZ3Qtv1dUBbFrywxyiba2Gq7UNvFPVaIaKNiDjSRY0+J88qyF3h6UWiceR3qP4lVihPTRRwXiXyBzHOmPq+LdJ76wb3MhyaijYcz0ESOvXJWaJCvw+Sz4uTy7F9C+Yj4q6l8RRHHRaL4KcRPITkHwNh4JSluGEklMnOY9aGJaONhAE3k1Mjjlk0HXxrBJktidB3U0+LKwsRPWMauhGb+GDN/jMUJAFicMA4qJb1Wd2wMDXcB3GOFiDYapnAQOdX6Kz48bMrNkLe0trZLeNjUTboWyr+I09K+G1nGroSa+jLPa29Yv3G4mLXU4ME+5nIQ0YbBGWgipyYnxbB46d8ncUW10FgLqFeJv5omTnEGuoQSZ5A4A+08AGjnjYNKSc+iiZ/CzGEAiI0hOce6HES0YTCAJnKqdUuV0OILtCq/2GrO4ZAv+TSUnWJUvfkmzkCXkKcWnlpIVQAgVRkHlZKn3jhquwDA2wLJx7ocRLRhMIWDyLEFMQe65lLgpVeFeelaAD8US3Oc/z5wRxGHVhnip4DUhoLZrC1psTEAqNmL5Bw89cbjBUm1WS/4XW49pP+Z9FwOb3Pm+cELbSRJRFT2OANN5NTkq2IAPf/iJM4IjYklBSOq2DP2w2KOrFJ4m+FtzsxNpo8V1Ow1oq7s3bypwvkTD3IemogqGgNoIse2iwU3JAnYLpnb5Bqg9QZLaQ5fEcdFVHmYy0FElYwBNJFzktiwCZgSMjgSMyom/9Gy82A1FxESmTGGJqKKxQCayLHzYnVnH6A+FBMaX/4q2n9T3N+78aNcRJiqDZyuz5A+nNC7OexM5U9PzmF9aCKqTAygiZyaekOcV16cmJp8ydLvXzDyNwtC28zXOAOdipn0TOjsA5r5sKOvQtMfDRe6pMA0IAEkMm+tLUET92xPnRUalhA/ZSyIFGS2SExYjuy3SGQ9biyMoYmoAnF9OpFTLa3AK6aWxKaW1mpAiJY3oXUz8JK58RLOQK/AkhuT71XG/PQUAHhbzHH2GmRPeHubgXRquwQAUq7EdsuHI/lSvypYZJZFWvLmTW8h5+yzAXCPFSKqNAygiZxqrZbFKcB4q3KFjBlzYwOUOrGnr63443Od9ESvZDc/XQjihHfq7Qz6P7E1qNUACTOH0diH80/AtxNyg9GoWaeQJUCD5Msxj55MvYVm9NTfPTkLT4N5GABknB9D9a7UWQ1SNRJnIG8D9BWvZfwN/2DfhfsQEZWNMv5+SlRu5i3RzznAWletGmqTOAeZeLpYg3Kx1c1bF/Dtcs0HSwCMDUSqLoOnLtNTWmEK2fbDMc95p/tI1XZ9gNqskn+6gv9SUTT+6SY8Bs5DE1FFYA40kVPKbssvnDsU9G8V2uT3Q9lryelMFm1Y7lXKBGj97ZKmf0htGdqyfc/4Kfh2IX4Knnqc/yG0GLTz0JaQnLMcC8YT4+bxrCOJ5DyS8wCMTOh0ynUs6xcyPU/aPhu7ojT2MR+aiCoFA2gix34kbo8itwAviV9E2y8CfvCG2PPGIo7LraTUsU5vJ8k5ZpQleOoh+YzH6muhLUKqgiRD8lmOKuOJfnMtkTmQROIsEmcBQFvMPAKovkocxkpz2xWFte2IqBIwgCZy7FcsM9CngTcsK8kkYFu10Ob596KNikpHiNdzh++SLxUuy/DUIjkPeABPJti1HkYAvZR1JBF/HfHXgVSxjnTJDlMKhyd1bAjeFuwDY2giKnMb5XsuUQl0bBEaEm8BX3j1D7abGs96oY4uCj1jT7KM3cazYgAN2XhMnIV3O+ABJEC2HJ7UEwAw7XAuVaHmOtRcBwDeizOPABIzlmGUOCO8aGb/GLVduOUuxtBEVM4YQBM59tDbYks1MHLT7502tSWfgnKV+Pf0mhtZxs6tTPXvLkgyHUZ2B8QydtlLA/XtaWyLTFeixj6jWDjzoYmojDGAJnLsnGW92iJG/u9HhdocsSeBD+4QOl7ZUcRxkavp29PU7F3vcRQBY2giKlcsY0fk2GbLX8ljaG2VMWEKoaV6qE9NCR1fe6mYA6sYOSpmCLUs7DcoMRdCBpCcTfWvNl+SSM3UJgq580hy1nhH7yVGyQu9FEbtDTY9tWVAQ+IMtDi0GLRlLD0Dzzb4dqXG+IaxOnDx+6j7KGrfZ87KAAB4GrD4nPEW8dfgvcR4BJCch2eT0S32lPGk5npo5xF/C55aQIKnBpIP8AL64lepfPdhSc7BUw8kAQ8QT6W7SABwy13+oabpEGvbEVF54Qw00ZpEnhGLQ8efVDEi1uuY/XPmQBOtyk3wD3EemojKi6RpJauiSlThbvJKQr7Gu7XH3pD2CdPN/uhj1QGx8R1R7RVLGvSpP8Lis6aW8w+jtsvYjFpbMiZW9doL+u4YxlydWXrHaT01Nt0/fgreZuOsfio7U1b/o79+Q/1RuI+wkXVsDN4W8a3TPbPvX7M380YbMrWASu9bhwu+x4rf7y/sDYnIPRhAEzl2m1f6diaAloHEbk17f5X0DdN8s/wBLR6skv7U3HiTFv+e5Yan7yvWUF1uKQoA8jbIFyFxxmiUL1rHEbnOzGHjSW2X8UtUbAw1e43NZay/BDpR6BiaATQRrRpzoIkcM+dAJwDEgGfEbA1sB/5NbJTeXdCRpCd6K8LM4UwUVRq+QOY54+Z10dgntuj/AdaytfjBPv8jTdzrm4jKAXOgiZyz+XONai2/u6zianG1llcsIb02lVV1QS9MRrR2rMtBROWBATSRY0+K6wVRA+WWRqHt4t9R8JrYMf5SsQb1/7d3/6Fx3/cdx19ffb86K5LlWpWzuE09p+bkOJ49Wjuw5gSBIYchhaxyIV4KdZ0t4Q7GhrRBSulcF4esdAQyqWUwaQSWtWWb/khdiu+gxKQN89G0TqDFUdrcjdQx7pqmaoKdyKp8X333x/d+fr8n3fd7ul+Sng8O5e5zn+/3+9HdKX7rrffn8wG2FmJoAB2AABoI7G7fKmCWlL3haXtfuvC6b2mOS80bFrDFEEMDaDcCaCCwt7xhsbldetlb7rw4p5EjvhKO/U0cF7DlHBMxNIA2IoAGAvPt5G3cpwu+ag07eUGf6vU09h5u2qjg59yUc7OwgUiucEML5X6dv7mLKqqwuqKz7M6/Xa+BZ8hDA2gjAmggsKNVNnIbuce7lI15eCT7P4uexg9eEVrH6JbRXfj/W1fhhhZy16rr2l7aJNK9Y5gNeC+Kq4w/7FvrAwBagn9UgMB+5cucWdLu/NIcViGQ7v5jpV/19rR/zE6EQOPt+u3t5KEBtB4BNBDYR6tkoPXX+WWGc/kaAfPQ4/r8fd6e5mHfNoRoHueWnFvSiiRppXBDC628n785y/kW945jN+C98KyKSD00gJZjIxWgftagss+/W9lm/+Kfs9mf+XLV17ISMXSrGNvc/0iSqv3ag2Yr7l9jFF7/rtskyehu8IX6vyBJD11jjxUArUQGGqhfbkFXvu+dnXbju1eiX/JtEbyb6LmV3KJnN4A2CrfN5d2/L22X3YGMSP5W+gXGvdPo98LaLWu3eo4ypxBAK5GBBgLbUaVtr39Fgb69evW6p637WFNGtNG49eL++MnzIgbLGRdrAwyz8hCncAmnkbGas5y/Ytf2/IBz70hS7lpFt56jcpZLM+dcK++ra3uN87uLVJQf6P2+Kvn3ygZ7fQNoFTLQQGDeqFiRQV35RL+3tf+K7uipbDL3HWnesAAUkIcG0BJkoIH6rUgjz796tzH0i1Lb4NlLI/riUmVH+zcvSyOtHVwnWi0fXFeZsifLW+UqDS0VyBckuExJsnaXvq49sJrp54B94OcuaVc+rZC17QA0HxlooH65BUn/+r8VbbdLyv7QW9jx7outGhOwpfQc9S7KIe2yv0keGkBTEUAD6/P0v1TOIsz89wVdueFbB/oq60C3klOot0a72IVb8Y1wyr42jrtYXu7Xpd1VJC29wtp2AJqKEg6gftag/uOr3mqN+c9lR2KWXquIqwe+xCocrbTp1tzYePxlOUbZ18ZxS1+6tlfU0rhp6YfFnEIATUIGGqhfbkHzS77W30k7vW2/n2/JgACUIw8NoDkIoIHAqi1j97UzvhljH4vquvfv1L9/uzlDArCGgWdYlwNAMxBAA4FVW8ZO3172NJof0oVLvhroH1EDDbQJeWgAjUYADaxL1rsRoWxbe+/xpqWtg9RAA+1DDA2goZhECNRveUFXfuNrzSn6SJ9SFZnp2+5t2aAAVHJ3bWROIYDGIQMNBFZ1K+8Bb4t5n/Qzb7XHzYXmDAlATUuv5Be5Iw8NoEEIoIHAfDXQ2wYVPeat1th5v/QDb0/nx80aFIAarDtl3SlJA8/o+BeIoQGsHwE0UL8uSdFtnsaVt3Xhpm8S4TUmEQJtYu0urRJt7WZdDgDrRw00ENgO7x4QtxakB/aYmjeknCRFpOUuaeTrxyPHvlNeBL3r8SqTCH9rnmzeYAGs6pGTu3btavcgAGxgBNBAYMPbNfte4YEp2V2SRv7iw+ZXfmcXW60DR6Tvvbisvo/og//LN1t3PVzlfPwTDgDARkQJBxBY7D6zYodi88MHJZ35m6Fiyzaz/6GzI8r2WypFz5Jyi+cp4QAAYJMggAYCi/7tJ3uLDwxTO/88JklnXn+gUNtx5z9cf35Eip46HSs7bu+zzmsTrAMNAMAmQQANBDf65OfUn78/+NHYl/8uHxUfHJR5+DnHcV4/I0nKnp9Llx125TEjkWrpQAEAQPMQQAMhjM4kT+TvfnrmYjGr/Gef1p88VpZzjk5cdFyZqZjiSceZGW31UAEAQJMYjuO0ewwAAADAhkEGGgAAAAiBZeyAdrjwmR3HvnOj3aMAtrwDZ51nrhnnximzAhAGJRxAi6USxthsuwcBoMyBs4UZwAAQBAE00HonB4xvvVe7G4CWiSdJQgMIjBpooPUe/dN+3bFHkfxDs/w5s0r/Gk+tcUgojToP1msjvBN1fFAbesXGXcQc/COiZwAhEUADrTfy/PWzA1e1nH9olz9nV+lf46k1DgmlUefBem2Ed6KOD2pDr9ioi/Te/8W7X2vQuQBsHZRwAAAAACGQgQYAAABCYBk7oO1SCeOpl5R+S8qpx9bSRvgDPtDJBndo4XqQjrGpTGlLUQAIigw00C6phOEam1X659KitLxK9GwWbs1V9gt1lWuZUkTaGfE8b0qRTpr0ZvbvlG/8DXj1zOAnMTvp9ViVWXy7TR05oI9HCg/K+3gOadpYGn3C2tHzjr2H+3p2mC//09jwdLbRlwew6VEDDbRYG9eBNgNPvbIs5XLNGYMpu1NT7MXXx43nOnWYgZimZG/sb8Gr/ONrqUdaCvwRrfjkV/4YWHfqr/6NJTgAhEQGGmix0RknMxXTx+9SbCrjOI7jnN1TrZ9ZdscsZId7e2uef41knl3tuar9w0XPbsY1UBbRDBjSNSIlaa1Zomb68+Z22Z2KeKsVItVHa5rqq6fUzg4SPTfvWzPNxp/cNq3iOXP56DngRSo++YXXpcc90zXNjhmGYSRSDRklgK2BABpovejExeQDvzyoyVPTWUln3nKc5CPeTrbkxgduNJeTzEEtLtY8uS1ZqwUW1UKqBiQp3ZRyoBMFTYo2InVa+C3APPqZO8qa8y+NvWzLOni3/4XytrQoi7tc/XcW29YHq/42s94YtXnfWqD4fU17zmYeilQ22TnfOateZN9oLEi/JUmKJ508ktAAQqCEAwAAAAiBDDQAAAAQAgE00HaphDE2li/BLC7NYRiGYQRcHyA7PZwv4cxOD4c8toGqDyPx2VJ1adl3t+/Lz37WqKa7u2qzsW/I2GUZfX9g7Our3gHrZwXrtq3buM3KfzUs4zbL6K480rKMVd7G+i/aTCzEASAkAmigc6QSxjeuFqsyM1OxP5w/VXtqU3b61OShpDMzqlRiaP50oaTzOZ1qaViw6jBe1OVfSVIqYXztpWL3N596/L+qnme1+YtvZrVga/EdvVm7Dhx1Cli3vJzTkp3/KltLtjzVybat4NNQ27RUiCkrssM0d/T0fqT3p8+OE0MDCIMaaKCNnrzH+MrPA/W8d48uXQ162ng+jjXOjdc/NerkgPGt9+o8FpuGpRChcLngaya2WmHqpdWr7be0FFH/NvXs1eiTzCMEEBgZaKCNzrzuvHC8v7SYQsSUaemAu7xdZip25Pgn82vd/eQt54Xj/bIsWZZMyTRlWeoZVGwqk4wXstbJeGwqk19PIHVuNj6+joDgm+86zx028xcqv2jxjv++ejTYI1NSeWPxW3PX44u94DjJ+DpeNYRV3AKmfC8YM1J7TcT+Pbn4o3eZkb7w631UXTOxI9imbNO0I103eheXeiLXb9c7e+64tK4fFgBbDhlooO2evMf4x2tauaXcUr7lwNSz908+9pJ0f7IiK+b2lLRyU44ls1vWoD4xmbm4/2nf7izxZCMSaicHjP9cktldcdHinfJGSSuL3j/ly93wzlJ3n5YW8g/tjz0a/+W/K5nRXw7Nvl1jAOa9Lzx29VjNbqjPJtgyphEa88MCYAshgAYAAABCoIQDAAAACIEAGgAAAAiBABoAAAAIgQAaAAAACIEAGgAAAAiBABrAppOdHg61O3MqUbYH+eoHrvZs8fBmjxMA0Bmsdg8AADpGdOKiM1HnswCALYMMNAAAABACATSAzcutkUhNDxt5ZZUWqUS+bXj6jcr+WW9pRXZ62DASqYrmNQ6v9jBbGoNhVKvbqOxQV0UIAKBFCKABbG7pyaf0nOM4jpOZis2OuaFpKmGMXZ7KOI7jOKfnJz3boEcfPBFLz50vBLnZ83Pp2NQTZXs9r324XyoxNHko6TiFYaQnn66IkLPTw0NzJzJOscflMWJoAOhcBNAANrn46YmoJDcu1uU3slLq3GypefSJqZjnkIoIOnt+Lh078WC09HStw72yQ084zkwhAC8No8Kh/cUrRCculvUHAHQaAmgAm1vs4JC3KfvG5fLm6IMn1oig/fFz7cO9Z4tGVVb0MTSZ9naYOB2fHaN6AwA2CAJoAPCLTpyOp+fOZ6vEz3VIJQyjVPSRqZKyHp1xqzeShUCaOBoAOhcBNIAtJ7r/kNLzmeLjzLw3JSxpdDyenjufOj+XLlZrhDm8XOrcbGwq41zMn2at/m4gnYxr9hwRNAB0KgJoAFvP6HhchfmESiXGqs4CHB2PpyfHJtPxcW818mqHR/cfUql0evpUWalGWfOw/3KerVg8NSIAgA5DAA1gCxqdKRVLPHVwKl6903hckj9+Xv3w0ZnMlCaH3Ern+dPJeKE5GU8XmudOZJLxigy2ez6NFRexG5o7kbk4sb6qEQBA8xiO47R7DAAAAMCGQQYaAAAACIEAGgAAAAiBABoAAAAIgQAaAAAACIEAGgAAAAiBABoAAAAIgQAaAAAACIEAGgAAAAiBABoAAAAI4f8BdEPhkHKd6hsAAAAASUVORK5CYII=" /><!-- --></p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="fu">ggRelatednessMatrix</span>(<span class="fu">as.matrix</span>(cn),</span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>  <span class="at">config =</span> <span class="fu">list</span>(</span>
+<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>    <span class="at">matrix_color_palette =</span> <span class="fu">c</span>(<span class="st">&quot;white&quot;</span>, <span class="st">&quot;lightblue&quot;</span>, <span class="st">&quot;blue&quot;</span>),</span>
+<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>    <span class="at">color_scale_midpoint =</span> <span class="fl">0.5</span>,</span>
+<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>    <span class="at">matrix_cluster =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a>    <span class="at">plot_title =</span> <span class="st">&quot;Common Nuclear Relatedness Matrix&quot;</span>,</span>
+<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a>    <span class="at">axis_x_label =</span> <span class="st">&quot;Individuals&quot;</span>,</span>
+<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a>    <span class="at">axis_y_label =</span> <span class="st">&quot;Individuals&quot;</span>,</span>
+<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a>    <span class="at">axis_text_size =</span> <span class="dv">8</span></span>
+<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a>  )</span>
+<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a>)</span></code></pre></div>
+<p><img role="img" 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" /><!-- --></p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">ggRelatednessMatrix</span>(<span class="fu">as.matrix</span>(mt),</span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>  <span class="at">config =</span> <span class="fu">list</span>(</span>
+<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>    <span class="at">matrix_color_palette =</span> <span class="fu">c</span>(<span class="st">&quot;white&quot;</span>, <span class="st">&quot;lightgreen&quot;</span>, <span class="st">&quot;darkgreen&quot;</span>),</span>
+<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>    <span class="at">color_scale_midpoint =</span> <span class="fl">0.5</span>,</span>
+<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>    <span class="at">matrix_cluster =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a>    <span class="at">plot_title =</span> <span class="st">&quot;Mitochondrial Relatedness Matrix&quot;</span>,</span>
+<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a>    <span class="at">axis_x_label =</span> <span class="st">&quot;Individuals&quot;</span>,</span>
+<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a>    <span class="at">axis_y_label =</span> <span class="st">&quot;Individuals&quot;</span>,</span>
+<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a>    <span class="at">axis_text_size =</span> <span class="dv">8</span></span>
+<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a>  )</span>
+<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a>)</span></code></pre></div>
+<p><img role="img" 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" /><!-- --></p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>))</span></code></pre></div>
 </div>
 <div id="convert-to-pairwise-format" class="section level2">
 <h2>Convert to Pairwise Format</h2>
@@ -444,12 +487,12 @@ <h2>Convert to Pairwise Format</h2>
 long-format tables using <code>com2links()</code>. This function returns
 a dataframe where each row represents a unique pair of individuals,
 including their additive and common nuclear coefficients.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>df_links <span class="ot">&lt;-</span> <span class="fu">com2links</span>(</span>
-<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>  <span class="at">writetodisk =</span> <span class="cn">FALSE</span>,</span>
-<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>  <span class="at">ad_ped_matrix =</span> add, <span class="at">cn_ped_matrix =</span> cn, <span class="at">mit_ped_matrix =</span> mt,</span>
-<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>  <span class="at">drop_upper_triangular =</span> <span class="cn">TRUE</span></span>
-<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>) <span class="co"># %&gt;%</span></span>
-<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co">#  filter(ID1 != ID2)</span></span></code></pre></div>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>df_links <span class="ot">&lt;-</span> <span class="fu">com2links</span>(</span>
+<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>  <span class="at">writetodisk =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>  <span class="at">ad_ped_matrix =</span> add, <span class="at">cn_ped_matrix =</span> cn, <span class="at">mit_ped_matrix =</span> mt,</span>
+<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a>  <span class="at">drop_upper_triangular =</span> <span class="cn">TRUE</span></span>
+<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>) <span class="co"># %&gt;%</span></span>
+<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a><span class="co">#  filter(ID1 != ID2)</span></span></code></pre></div>
 <p>The function can return the entire matrix or just the lower
 triangular part, which is often sufficient for our purposes. Setting
 <code>drop_upper_triangular = TRUE</code> ensures we only retain one
@@ -461,23 +504,23 @@ <h2>Locate Jon and Daenerys</h2>
 <p>We next identify the rows in the pairwise relatedness table that
 correspond to Jon Snow and Daenerys Targaryen. First, we retrieve their
 individual IDs:</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Find the IDs of Jon Snow and Daenerys Targaryen</span></span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>jon_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Jon Snow&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span>
-<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a></span>
-<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a>dany_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
-<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Daenerys Targaryen&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span></code></pre></div>
+<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co"># Find the IDs of Jon Snow and Daenerys Targaryen</span></span>
+<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a></span>
+<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>jon_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
+<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Jon Snow&quot;</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span>
+<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a></span>
+<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a>dany_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
+<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Daenerys Targaryen&quot;</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span></code></pre></div>
 <p>We can then filter the pairwise relatedness table to isolate the dyad
 of interest:</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>jon_dany_row <span class="ot">&lt;-</span> df_links <span class="sc">%&gt;%</span></span>
-<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">==</span> jon_id <span class="sc">|</span> ID2 <span class="sc">==</span> jon_id) <span class="sc">%&gt;%</span></span>
-<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">%in%</span> dany_id <span class="sc">|</span> ID2 <span class="sc">%in%</span> dany_id) <span class="sc">%&gt;%</span> <span class="co"># round to nearest 4th decimal</span></span>
-<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">c</span>(addRel, mitRel, cnuRel), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>)))</span>
-<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a></span>
-<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a>jon_dany_row</span></code></pre></div>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>jon_dany_row <span class="ot">&lt;-</span> df_links <span class="sc">%&gt;%</span></span>
+<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">==</span> jon_id <span class="sc">|</span> ID2 <span class="sc">==</span> jon_id) <span class="sc">%&gt;%</span></span>
+<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">%in%</span> dany_id <span class="sc">|</span> ID2 <span class="sc">%in%</span> dany_id) <span class="sc">%&gt;%</span> <span class="co"># round to nearest 4th decimal</span></span>
+<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">c</span>(addRel, mitRel, cnuRel), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>)))</span>
+<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a></span>
+<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a>jon_dany_row</span></code></pre></div>
 <pre><code>##   ID1 ID2 addRel mitRel cnuRel
 ## 1 206 211 0.5031      0      0
 ## 2 211 304 0.0562      0      0</code></pre>
@@ -491,19 +534,19 @@ <h2>Locate Jon and Daenerys</h2>
 as Rhaenyra Targaryen and Damemon Targaryen, we can see how the
 relatedness coefficients vary across different characters in the
 dataset.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>rhaenyra_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
-<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Rhaenyra Targaryen&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span>
-<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a>daemon_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
-<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Daemon Targaryen&quot;</span>) <span class="sc">%&gt;%</span></span>
-<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span>
-<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a></span>
-<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a>rhaenyra_daemon_row <span class="ot">&lt;-</span> df_links <span class="sc">%&gt;%</span></span>
-<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">==</span> rhaenyra_id <span class="sc">|</span> ID2 <span class="sc">==</span> rhaenyra_id) <span class="sc">%&gt;%</span></span>
-<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">%in%</span> daemon_id <span class="sc">|</span> ID2 <span class="sc">%in%</span> daemon_id) <span class="sc">%&gt;%</span> <span class="co"># round to 4th decimal</span></span>
-<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">c</span>(addRel, mitRel, cnuRel), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>)))</span>
-<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a></span>
-<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a>rhaenyra_daemon_row</span></code></pre></div>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>rhaenyra_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Rhaenyra Targaryen&quot;</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span>
+<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a>daemon_id <span class="ot">&lt;-</span> df_got <span class="sc">%&gt;%</span></span>
+<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>  <span class="fu">filter</span>(name <span class="sc">==</span> <span class="st">&quot;Daemon Targaryen&quot;</span>) <span class="sc">%&gt;%</span></span>
+<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a>  <span class="fu">pull</span>(ID)</span>
+<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a></span>
+<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a>rhaenyra_daemon_row <span class="ot">&lt;-</span> df_links <span class="sc">%&gt;%</span></span>
+<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">==</span> rhaenyra_id <span class="sc">|</span> ID2 <span class="sc">==</span> rhaenyra_id) <span class="sc">%&gt;%</span></span>
+<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a>  <span class="fu">filter</span>(ID1 <span class="sc">%in%</span> daemon_id <span class="sc">|</span> ID2 <span class="sc">%in%</span> daemon_id) <span class="sc">%&gt;%</span> <span class="co"># round to 4th decimal</span></span>
+<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">c</span>(addRel, mitRel, cnuRel), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>)))</span>
+<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a></span>
+<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a>rhaenyra_daemon_row</span></code></pre></div>
 <pre><code>##   ID1 ID2 addRel mitRel cnuRel
 ## 1 339 536 0.7355      1      0</code></pre>
 <p>Similarly, we can see that Rhaenyra and Daemon have an additive
@@ -527,18 +570,18 @@ <h2>Plotting the Pedigree with Incomplete Parental Information</h2>
 <p>To facilitate plotting, we check for individuals with one known
 parent but a missing other. For those cases, we assign a placeholder ID
 to the missing parent.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>df_repaired <span class="ot">&lt;-</span> <span class="fu">checkParentIDs</span>(df_got, <span class="co"># %&gt;% filter(famID == 1),</span></span>
-<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>  <span class="at">addphantoms =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>  <span class="at">repair =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a>  <span class="at">parentswithoutrow =</span> <span class="cn">FALSE</span>,</span>
-<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a>  <span class="at">repairsex =</span> <span class="cn">FALSE</span></span>
-<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a>) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(</span>
-<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a>  <span class="co"># famID = 1,</span></span>
-<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a>  <span class="at">affected =</span> <span class="fu">case_when</span>(</span>
-<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a>    ID <span class="sc">%in%</span> <span class="fu">c</span>(jon_id, dany_id, <span class="dv">339</span>) <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
-<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a>    <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">FALSE</span></span>
-<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a>  )</span>
-<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a>)</span></code></pre></div>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>df_repaired <span class="ot">&lt;-</span> <span class="fu">checkParentIDs</span>(df_got, <span class="co"># %&gt;% filter(famID == 1),</span></span>
+<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a>  <span class="at">addphantoms =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a>  <span class="at">repair =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a>  <span class="at">parentswithoutrow =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a>  <span class="at">repairsex =</span> <span class="cn">FALSE</span></span>
+<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a>) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(</span>
+<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a>  <span class="co"># famID = 1,</span></span>
+<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a>  <span class="at">affected =</span> <span class="fu">case_when</span>(</span>
+<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a>    ID <span class="sc">%in%</span> <span class="fu">c</span>(jon_id, dany_id, <span class="dv">339</span>) <span class="sc">~</span> <span class="cn">TRUE</span>,</span>
+<span id="cb16-10"><a href="#cb16-10" tabindex="-1"></a>    <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">FALSE</span></span>
+<span id="cb16-11"><a href="#cb16-11" tabindex="-1"></a>  )</span>
+<span id="cb16-12"><a href="#cb16-12" tabindex="-1"></a>)</span></code></pre></div>
 <pre><code>## REPAIR IN EARLY ALPHA</code></pre>
 <p>This code creates new IDs for individuals with one known parent and a
 missing other. It checks if either <code>momID</code> or
@@ -546,7 +589,7 @@ <h2>Plotting the Pedigree with Incomplete Parental Information</h2>
 the row number. This allows us to visualize the pedigree even when some
 parental information is incomplete. Now we can check the repaired
 pedigree for unique IDs and parent-child relationships.</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>checkIDs <span class="ot">&lt;-</span> <span class="fu">checkIDs</span>(df_repaired, <span class="at">verbose =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>checkIDs <span class="ot">&lt;-</span> <span class="fu">checkIDs</span>(df_repaired, <span class="at">verbose =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
 <pre><code>## Standardizing column names...</code></pre>
 <pre><code>## Checking IDs...
 ## Step 1: Checking for unique IDs...
@@ -555,18 +598,18 @@ <h2>Plotting the Pedigree with Incomplete Parental Information</h2>
 ## No within row duplicates found.
 ## Validation Results:</code></pre>
 <pre><code>## TRUE0NULL0000FALSENULLNULLNULL</code></pre>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="co"># checkIDs</span></span></code></pre></div>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="co"># Check for unique IDs and parent-child relationships</span></span>
-<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a>checkPedigreeNetwork <span class="ot">&lt;-</span> <span class="fu">checkPedigreeNetwork</span>(df_repaired,</span>
-<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>  <span class="at">personID =</span> <span class="st">&quot;ID&quot;</span>,</span>
-<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a>  <span class="at">momID =</span> <span class="st">&quot;momID&quot;</span>,</span>
-<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a>  <span class="at">dadID =</span> <span class="st">&quot;dadID&quot;</span>,</span>
-<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a>  <span class="at">verbose =</span> <span class="cn">TRUE</span></span>
-<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a>)</span></code></pre></div>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="co"># checkIDs</span></span></code></pre></div>
+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="co"># Check for unique IDs and parent-child relationships</span></span>
+<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>checkPedigreeNetwork <span class="ot">&lt;-</span> <span class="fu">checkPedigreeNetwork</span>(df_repaired,</span>
+<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a>  <span class="at">personID =</span> <span class="st">&quot;ID&quot;</span>,</span>
+<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a>  <span class="at">momID =</span> <span class="st">&quot;momID&quot;</span>,</span>
+<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a>  <span class="at">dadID =</span> <span class="st">&quot;dadID&quot;</span>,</span>
+<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a>  <span class="at">verbose =</span> <span class="cn">TRUE</span></span>
+<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a>)</span></code></pre></div>
 <pre><code>## No individuals with more than two parents detected.</code></pre>
 <pre><code>## No duplicate edges detected.</code></pre>
 <pre><code>## No cyclic relationships detected.</code></pre>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>checkPedigreeNetwork</span></code></pre></div>
+<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>checkPedigreeNetwork</span></code></pre></div>
 <pre><code>## $individuals_with_excess_parents
 ## character(0)
 ## 
@@ -596,47 +639,47 @@ <h2>Visualize the Pedigree</h2>
 visualize pedigrees, allowing for easy integration with other
 <code>ggplot2</code> functions relative to kinship2’s pedigree plotting
 functions.</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">library</span>(ggpedigree)</span>
-<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a></span>
-<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a>df_repaired_renamed <span class="ot">&lt;-</span> df_repaired <span class="sc">%&gt;%</span> <span class="fu">rename</span>(</span>
-<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a>  <span class="at">personID =</span> ID</span>
-<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a>)</span>
-<span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a>plt <span class="ot">&lt;-</span> <span class="fu">ggpedigree</span>(df_repaired_renamed,</span>
-<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a>  <span class="at">overlay_column =</span> <span class="st">&quot;affected&quot;</span>,</span>
-<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a>  <span class="at">personID =</span> <span class="st">&quot;personID&quot;</span>,</span>
-<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a>  <span class="at">interactive =</span> <span class="cn">FALSE</span>,</span>
-<span id="cb25-10"><a href="#cb25-10" tabindex="-1"></a>  <span class="at">config =</span> <span class="fu">list</span>(</span>
-<span id="cb25-11"><a href="#cb25-11" tabindex="-1"></a>    <span class="at">overlay_include =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb25-12"><a href="#cb25-12" tabindex="-1"></a>    <span class="at">point_size =</span> .<span class="dv">75</span>,</span>
-<span id="cb25-13"><a href="#cb25-13" tabindex="-1"></a>    <span class="at">code_male =</span> <span class="st">&quot;M&quot;</span>,</span>
-<span id="cb25-14"><a href="#cb25-14" tabindex="-1"></a>    <span class="at">ped_width =</span> <span class="dv">17</span>,</span>
-<span id="cb25-15"><a href="#cb25-15" tabindex="-1"></a>    <span class="at">label_nudge_y =</span> <span class="sc">-</span>.<span class="dv">25</span>,</span>
-<span id="cb25-16"><a href="#cb25-16" tabindex="-1"></a>    <span class="at">include_labels =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb25-17"><a href="#cb25-17" tabindex="-1"></a>    <span class="at">label_method =</span> <span class="st">&quot;geom_text&quot;</span>,</span>
-<span id="cb25-18"><a href="#cb25-18" tabindex="-1"></a>    <span class="co"># segment_self_color = &quot;purple&quot;,</span></span>
-<span id="cb25-19"><a href="#cb25-19" tabindex="-1"></a>    <span class="at">sex_color_include =</span> <span class="cn">FALSE</span>,</span>
-<span id="cb25-20"><a href="#cb25-20" tabindex="-1"></a>    <span class="at">focal_fill_personID =</span> <span class="dv">353</span>, <span class="co"># 339, # 353,</span></span>
-<span id="cb25-21"><a href="#cb25-21" tabindex="-1"></a>    <span class="at">focal_fill_include =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb25-22"><a href="#cb25-22" tabindex="-1"></a>    <span class="at">tooltip_columns =</span> <span class="fu">c</span>(<span class="st">&quot;personID&quot;</span>, <span class="st">&quot;name&quot;</span>, <span class="st">&quot;focal_fill&quot;</span>),</span>
-<span id="cb25-23"><a href="#cb25-23" tabindex="-1"></a>    <span class="at">focal_fill_force_zero =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb25-24"><a href="#cb25-24" tabindex="-1"></a>    <span class="at">focal_fill_mid_color =</span> <span class="st">&quot;orange&quot;</span>,</span>
-<span id="cb25-25"><a href="#cb25-25" tabindex="-1"></a>    <span class="at">focal_fill_low_color =</span> <span class="st">&quot;#9F2A63FF&quot;</span>,</span>
-<span id="cb25-26"><a href="#cb25-26" tabindex="-1"></a>    <span class="at">focal_fill_legend_title =</span> <span class="st">&quot;Relatedness to </span><span class="sc">\n</span><span class="st">Aegon Targaryen&quot;</span>,</span>
-<span id="cb25-27"><a href="#cb25-27" tabindex="-1"></a>    <span class="at">focal_fill_na_value =</span> <span class="st">&quot;black&quot;</span>,</span>
-<span id="cb25-28"><a href="#cb25-28" tabindex="-1"></a>    <span class="at">value_rounding_digits =</span> <span class="dv">4</span></span>
-<span id="cb25-29"><a href="#cb25-29" tabindex="-1"></a>  )</span>
-<span id="cb25-30"><a href="#cb25-30" tabindex="-1"></a>)</span>
-<span id="cb25-31"><a href="#cb25-31" tabindex="-1"></a></span>
-<span id="cb25-32"><a href="#cb25-32" tabindex="-1"></a>plt</span></code></pre></div>
-<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="co"># reduce file size for CRAN</span></span>
-<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a><span class="co"># if (interactive()) {</span></span>
-<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a><span class="co"># If running interactively, use plotly::partial_bundle</span></span>
-<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a><span class="co"># to reduce file size for CRAN</span></span>
-<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a><span class="co">#  plotly::partial_bundle(plt)</span></span>
-<span id="cb26-6"><a href="#cb26-6" tabindex="-1"></a><span class="co"># } else {</span></span>
-<span id="cb26-7"><a href="#cb26-7" tabindex="-1"></a><span class="co">#  plotly::partial_bundle(plt, local = TRUE)</span></span>
-<span id="cb26-8"><a href="#cb26-8" tabindex="-1"></a><span class="co"># }</span></span></code></pre></div>
+<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" tabindex="-1"></a><span class="fu">library</span>(ggpedigree)</span>
+<span id="cb29-2"><a href="#cb29-2" tabindex="-1"></a></span>
+<span id="cb29-3"><a href="#cb29-3" tabindex="-1"></a>df_repaired_renamed <span class="ot">&lt;-</span> df_repaired <span class="sc">%&gt;%</span> <span class="fu">rename</span>(</span>
+<span id="cb29-4"><a href="#cb29-4" tabindex="-1"></a>  <span class="at">personID =</span> ID</span>
+<span id="cb29-5"><a href="#cb29-5" tabindex="-1"></a>)</span>
+<span id="cb29-6"><a href="#cb29-6" tabindex="-1"></a>plt <span class="ot">&lt;-</span> <span class="fu">ggpedigree</span>(df_repaired_renamed,</span>
+<span id="cb29-7"><a href="#cb29-7" tabindex="-1"></a>  <span class="at">overlay_column =</span> <span class="st">&quot;affected&quot;</span>,</span>
+<span id="cb29-8"><a href="#cb29-8" tabindex="-1"></a>  <span class="at">personID =</span> <span class="st">&quot;personID&quot;</span>,</span>
+<span id="cb29-9"><a href="#cb29-9" tabindex="-1"></a>  <span class="at">interactive =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb29-10"><a href="#cb29-10" tabindex="-1"></a>  <span class="at">config =</span> <span class="fu">list</span>(</span>
+<span id="cb29-11"><a href="#cb29-11" tabindex="-1"></a>    <span class="at">overlay_include =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb29-12"><a href="#cb29-12" tabindex="-1"></a>    <span class="at">point_size =</span> .<span class="dv">75</span>,</span>
+<span id="cb29-13"><a href="#cb29-13" tabindex="-1"></a>    <span class="at">code_male =</span> <span class="st">&quot;M&quot;</span>,</span>
+<span id="cb29-14"><a href="#cb29-14" tabindex="-1"></a>    <span class="at">ped_width =</span> <span class="dv">17</span>,</span>
+<span id="cb29-15"><a href="#cb29-15" tabindex="-1"></a>    <span class="at">label_nudge_y =</span> <span class="sc">-</span>.<span class="dv">25</span>,</span>
+<span id="cb29-16"><a href="#cb29-16" tabindex="-1"></a>    <span class="at">include_labels =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb29-17"><a href="#cb29-17" tabindex="-1"></a>    <span class="at">label_method =</span> <span class="st">&quot;geom_text&quot;</span>,</span>
+<span id="cb29-18"><a href="#cb29-18" tabindex="-1"></a>    <span class="co"># segment_self_color = &quot;purple&quot;,</span></span>
+<span id="cb29-19"><a href="#cb29-19" tabindex="-1"></a>    <span class="at">sex_color_include =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb29-20"><a href="#cb29-20" tabindex="-1"></a>    <span class="at">focal_fill_personID =</span> <span class="dv">353</span>, <span class="co"># 339, # 353,</span></span>
+<span id="cb29-21"><a href="#cb29-21" tabindex="-1"></a>    <span class="at">focal_fill_include =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb29-22"><a href="#cb29-22" tabindex="-1"></a>    <span class="at">tooltip_columns =</span> <span class="fu">c</span>(<span class="st">&quot;personID&quot;</span>, <span class="st">&quot;name&quot;</span>, <span class="st">&quot;focal_fill&quot;</span>),</span>
+<span id="cb29-23"><a href="#cb29-23" tabindex="-1"></a>    <span class="at">focal_fill_force_zero =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb29-24"><a href="#cb29-24" tabindex="-1"></a>    <span class="at">focal_fill_mid_color =</span> <span class="st">&quot;orange&quot;</span>,</span>
+<span id="cb29-25"><a href="#cb29-25" tabindex="-1"></a>    <span class="at">focal_fill_low_color =</span> <span class="st">&quot;#9F2A63FF&quot;</span>,</span>
+<span id="cb29-26"><a href="#cb29-26" tabindex="-1"></a>    <span class="at">focal_fill_legend_title =</span> <span class="st">&quot;Relatedness to </span><span class="sc">\n</span><span class="st">Aegon Targaryen&quot;</span>,</span>
+<span id="cb29-27"><a href="#cb29-27" tabindex="-1"></a>    <span class="at">focal_fill_na_value =</span> <span class="st">&quot;black&quot;</span>,</span>
+<span id="cb29-28"><a href="#cb29-28" tabindex="-1"></a>    <span class="at">value_rounding_digits =</span> <span class="dv">4</span></span>
+<span id="cb29-29"><a href="#cb29-29" tabindex="-1"></a>  )</span>
+<span id="cb29-30"><a href="#cb29-30" tabindex="-1"></a>)</span>
+<span id="cb29-31"><a href="#cb29-31" tabindex="-1"></a></span>
+<span id="cb29-32"><a href="#cb29-32" tabindex="-1"></a>plt</span></code></pre></div>
+<p><img role="img" 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" /><!-- --></p>
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="co"># reduce file size for CRAN</span></span>
+<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a><span class="co"># if (interactive()) {</span></span>
+<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a><span class="co"># If running interactively, use plotly::partial_bundle</span></span>
+<span id="cb30-4"><a href="#cb30-4" tabindex="-1"></a><span class="co"># to reduce file size for CRAN</span></span>
+<span id="cb30-5"><a href="#cb30-5" tabindex="-1"></a><span class="co">#  plotly::partial_bundle(plt)</span></span>
+<span id="cb30-6"><a href="#cb30-6" tabindex="-1"></a><span class="co"># } else {</span></span>
+<span id="cb30-7"><a href="#cb30-7" tabindex="-1"></a><span class="co">#  plotly::partial_bundle(plt, local = TRUE)</span></span>
+<span id="cb30-8"><a href="#cb30-8" tabindex="-1"></a><span class="co"># }</span></span></code></pre></div>
 <p>This plot can provide an interactive visualization of the ASOIAF
 pedigree (if interactive is set to TRUE), allowing users to explore
 relationships and affected status. The focal individual is highlighted,
@@ -652,6 +695,7 @@ <h2>Conclusion</h2>
 pedigree, we provided a comprehensive overview of how related Jon Snow
 and Daenerys Targaryen truly are.</p>
 </div>
+</div>