.ComputeClusterStats update; add pattern method to outer functions.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: NAIR
 Type: Package
 Title: Network Analysis of Immune Repertoire
-Version: 1.0.3.9002
+Version: 1.0.4.9001
 Authors@R: c(
     person("Brian", "Neal", email = "[email protected]", role = c("aut", "cre")),
     person("Hai", "Yang", email = "[email protected]", role = "aut"),

NEWS.md

@@ -1,18 +1,32 @@
 # NAIR Current Development Version
 
+## Breaking Changes
+
+* `buildRepSeqNetwork()` now removes data rows with `NA` count values (with a warning) prior to network building when count data is specified via the `count_cols` parameter. This is a reversion to behavior that existed prior to the initial release version 1.0.0. 
+
 ## New Features
 
-* New internal cpp function that computes graph adjacency matrix using pattern-based algorithm, developed by Daniil Matveev (implemented for metrics Hamming, Levenshtein and cutoffs 0, 1, 2). Faster than the default algorithm when network is sufficiently sparse and sequences are not too long, but can incur memory issues with large or densely-connected networks.
-* `generateAdjacencyMatrix()` has new parameter `method` used to specify the algorithm. Accepts value `"pattern"` to call the new routine for the pattern-based algorithm.
-* Speed improvements to the default algorithms for computing graph adjacency matrices
-* Speed improvement to the argument check used for function parameters that accept an adjacency matrix
+* New internal cpp function to compute graph adjacency matrix using pattern-based algorithm developed by Daniil Matveev (implemented for metrics Hamming, Levenshtein and cutoffs 0, 1, 2).
+* `generateAdjacencyMatrix()` and `generateNetworkObjects()` have new parameter `method` used to specify the algorithm. Accepts value `"pattern"` to call the new routine for the pattern-based algorithm.
+* `buildRepSeqNetwork()` has new parameter `net_build_method` used to specify the algorithm.
+* Speed improvements:
+    * Default algorithms for computing graph adjacency matrices
+    * Computation of cluster properties
+    * Argument check for function parameters that accept an adjacency matrix
 
 ## Minor Changes and Bug Fixes
 
-* Saving network output using `output_type = "individual"` now also saves the entire network list as an RData file (`.rda`).
-* Updated tests for compatibility with upcoming changes to guides in `ggplot2` (thanks to Teun van den Brand and the `ggplot2` development team for contributing the updates)
+* `filterInputData()` parameter `count_col` is no longer deprecated; when provided, data rows with `NA` count values are removed with a warning.
+
 
 
+# NAIR 1.0.4
+
+## Minor Changes and Bug Fixes
+
+* Updated tests for compatibility with changes to guides in `ggplot2` (thanks to Teun van den Brand and the `ggplot2` development team for contributing the updates)
+* Saving network output using `output_type = "individual"` now also saves the entire network list as an RData file (`.rda`).
+
 
 
 # NAIR 1.0.3

R/arghecks_upper.R

@@ -229,6 +229,9 @@
 }
 
 .matchMethod <- function(x, cutoff) {
+  if (cutoff == 0) {
+    return("pattern")
+  }
   if (pmatch(x, "pattern", 0)) {
     if (!any(cutoff == c(0, 1, 2))) {
       warning(

R/main_function.R

@@ -24,6 +24,7 @@ buildRepSeqNetwork <- function(
     dist_type = "hamming",
     dist_cutoff = 1,
     drop_isolated_nodes = TRUE,
+    net_build_method = "default",
     node_stats = FALSE,
     stats_to_include = chooseNodeStats(),
     cluster_stats = FALSE,
@@ -55,6 +56,7 @@ buildRepSeqNetwork <- function(
   drop_matches <- .check(drop_matches, .isString, NULL, ornull = TRUE)
   dist_type <- .checkDistType(dist_type, "hamming")
   dist_cutoff <- .check(dist_cutoff, .isNonneg, 1)
+  net_build_method <- .checkMethod(net_build_method, dist_cutoff)
   drop_isolated_nodes <- .checkTF(drop_isolated_nodes, TRUE)
   node_stats <- .checkTF(node_stats, FALSE)
   if (isTRUE(node_stats)) {
@@ -105,8 +107,14 @@ buildRepSeqNetwork <- function(
   }
 
   # Build network
-  net <- generateNetworkObjects(data, seq_col, dist_type, dist_cutoff,
-                                drop_isolated_nodes, verbose
+  net <- generateNetworkObjects(
+    data,
+    seq_col,
+    dist_type = dist_type,
+    dist_cutoff = dist_cutoff,
+    drop_isolated_nodes = drop_isolated_nodes,
+    method = net_build_method,
+    verbose = verbose
   )
   if (is.null(net)) {
     warning("Graph contains no nodes; returning NULL. ",