R-Computing-Lab · smasongarrison · Jan 28, 2026 · Jan 7, 2026 · Jan 9, 2026 · Jan 14, 2026
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: BGmisc
 Title: An R Package for Extended Behavior Genetics Analysis
-Version: 1.5.2
+Version: 1.6.0
 Authors@R: c(
     person("S. Mason", "Garrison", , "garrissm@wfu.edu", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-4804-6003")),

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,11 @@
+# BGmisc NEWS
+
+# BGmisc 1.6
+* Optimize  simulatePedigree and helpers for speed and memory usage
+* Major gains in speed for deeper pedigrees
+* Added more tests for simulatePedigree
+* Fix error when not enough single people available
+
 # BGmisc 1.5.2
 * More flexible ID generation for simulatePedigree
 * Created ped2gen function to extract generation information from pedigree data.frames

diff --git a/R/buildComponent.R b/R/buildComponent.R
@@ -1,9 +1,7 @@
 #' Take a pedigree and turn it into a relatedness matrix
 #' @param ped a pedigree dataset.  Needs ID, momID, and dadID columns
 #' @param component character.  Which component of the pedigree to return.  See Details.
-#' @param max_gen the maximum number of generations to compute
-#'  (e.g., only up to 4th degree relatives).  The default is 25. However it can be set to infinity.
-#'   `Inf` uses as many generations as there are in the data.
+#' @param max_gen the maximum number of iterations that the adjacency matrix is multiplied to get the relatedness matrix. `Inf` uses as many iterations as there are in the data. Defaults to 25.
 #' @param sparse logical.  If TRUE, use and return sparse matrices from Matrix package
 #' @param verbose logical.  If TRUE, print progress through stages of algorithm
 #' @param update_rate numeric. The rate at which to print progress
@@ -100,17 +98,22 @@ ped2com <- function(ped, component,
     "tcrossprod", "crossprod", "star",
     "tcross.alt.crossprod", "tcross.alt.star"
   )
+
   if (!config$transpose_method %in% transpose_method_options) {
     stop(paste0(
       "Invalid method specified. Choose from ",
       paste(transpose_method_options, collapse = ", "), "."
     ))
   }
 
-
-  if (!config$adjacency_method %in%
-    c("indexed", "loop", "direct", "beta")) {
-    stop("Invalid method specified. Choose from 'indexed', 'loop', 'direct', or 'beta'.")
+  # Validate the 'adjacency_method' argument
+  adjacency_method_options <- c("indexed", "loop", "direct", "beta")
+  if (!config$adjacency_method %in% adjacency_method_options
+  ) {
+    stop(paste0(
+      "Invalid method specified. Choose from ",
+      paste(adjacency_method_options, collapse = ", "), "."
+    ))
   }
 
   # standardize colnames
@@ -215,12 +218,21 @@ ped2com <- function(ped, component,
     count <- 0
   }
   maxCount <- config$max_gen + 1
+
   if (config$verbose == TRUE) {
     cat("About to do RAM path tracing\n")
   }
 
   # 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
+
+  # how much percision do we need to get unbiased estimates
+
+  # big matrix still happens here because the network is built. just less percise on inbreeding
+
+  # bias-precision tradeoff. how much percision do we need to get unbiased estimates? not a lot
-  # 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
-
-  # how much percision do we need to get unbiased estimates
-
-  # big matrix still happens here because the network is built. just less percise on inbreeding
-
-  # bias-precision tradeoff. how much percision do we need to get unbiased estimates? not a lot
+  # r accumulates I + A + A^2 + ... ≈ (I - A)^-1 from RAM path tracing
+  # Iterate until no additional relatedness is contributed (mtSum == 0) or max_gen is reached.
+  # max_gen controls the bias–precision tradeoff: larger values give more precise but more expensive estimates.
-  # 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
-
-  # how much percision do we need to get unbiased estimates
-
-  # big matrix still happens here because the network is built. just less percise on inbreeding
-
-  # bias-precision tradeoff. how much percision do we need to get unbiased estimates? not a lot
+  # r accumulates I + A + A^2 + ... ≈ (I - A)^-1 from RAM path tracing
+  # Iterate until no additional relatedness is contributed (mtSum == 0) or max_gen is reached.
+  # max_gen controls the bias–precision tradeoff: larger values give more precise but more expensive estimates.
   while (mtSum != 0 && count < maxCount) {
     r <- r + newIsPar
     gen <- gen + (Matrix::rowSums(newIsPar) > 0)

diff --git a/R/helpPedigree.R b/R/helpPedigree.R