CRAN submission.

DESCRIPTION

@@ -1,10 +1,11 @@
 Package: dbscan
-Version: 1.1-12-1
-Date: 2023-xx-xx
+Version: 1.2-0
+Date: 2024-06-28
 Title: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) 
     and Related Algorithms
 Authors@R: c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
-                email = "[email protected]"),
+                email = "[email protected]",
+                comment = c(ORCID = "0000-0003-2716-1405")),
 	    person("Matthew", "Piekenbrock", role = c("aut", "cph")),
 	    person("Sunil", "Arya", role = c("ctb", "cph")),
 	    person("David", "Mount", role = c("ctb", "cph")))

NEWS.md

@@ -1,18 +1,19 @@
-# dbscan 1.1-12-1 (2024-xx-xx)
+# dbscan 1.2-0 (2024-06-28)
 
 ## New Features
+* dbscan has now tidymodels tidiers (glance, tidy, augment).
 * kNNdistplot can now plot a range of k/minPts values.
 * added stats::nobs methods for the clusterings.
 * kNN and frNN now contains the used distance metric.
-* dbscan has now tidymodels tidiers (glance, tidy, augment).
 
 ## Changes
-* Reorganized the C++ source code.
-* README now used bibtex.
+* dbscan component dist was renamed to metric. 
 * Removed redundant sort in kNNdistplot (reported by Natasza Szczypien).
-* Refactor: use more performant anyNA(x) instead of any(is.na(x)) (by m-muecke).
+* Refactoring use more performant anyNA(x) instead of any(is.na(x))
+  and many more (by m-muecke).
+* Reorganized the C++ source code.
+* README now uses bibtex.
 * Tests use now testthat edition 3 (m-muecke).
-* dbscan component dist was renamed to metric. 
 
 # dbscan 1.1-12 (2023-11-28)
 

R/hdbscan.R

@@ -178,7 +178,6 @@ hdbscan <- function(x,
   ## Generate membership 'probabilities' using core distance as the measure of density
   prob <- rep(0, length(cl))
   for (cid in sl) {
-    ccl <- res[[as.character(cid)]]
     max_f <- max(coredist[which(cl == cid)])
     pr <- (max_f - coredist[which(cl == cid)]) / max_f
     prob[cl == cid] <- pr

README.Rmd

@@ -103,13 +103,13 @@ library(tidyverse)
 db <- x %>% dbscan(eps = .42, minPts = 5)
 ```
 
-Get cluster statistics
+Get cluster statistics as a tibble
 
 ```{r tidyverse2}
 tidy(db)
 ```
 
-Visualize the clustering with ggplot2
+Visualize the clustering with ggplot2 (use an x for noise points)
 ```{r tidyverse3}
 augment(db, x) %>% 
   ggplot(aes(x = Petal.Length, y = Petal.Width)) +

README.md

@@ -238,21 +238,21 @@ db <- x %>%
     dbscan(eps = 0.42, minPts = 5)
 ```
 
-Get cluster statistics
+Get cluster statistics as a tibble
 
 ``` r
 tidy(db)
 ```
 
     ## # A tibble: 4 × 3
     ##   cluster  size noise
-    ##   <fct>   <int> <fct>
+    ##   <fct>   <int> <lgl>
     ## 1 0          29 TRUE 
     ## 2 1          48 FALSE
     ## 3 2          37 FALSE
     ## 4 3          36 FALSE
 
-Visualize the clustering with ggplot2
+Visualize the clustering with ggplot2 (use an x for noise points)
 
 ``` r
 augment(db, x) %>%