New release.

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: streamMOA
-Version: 1.2-3.1
-Date: 2020-xx-xx
+Version: 1.2-4
+Date: 2022-05-09
 Encoding: UTF-8
 Title: Interface for MOA Stream Clustering Algorithms
 Authors@R: c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
@@ -10,7 +10,7 @@ Authors@R: c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
 	person("Matthias", "Carnein", role = c("ctb")),
 	person("Dalibor", "Krleža", role = c("ctb")))
 Description: Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).
-Depends: stream (>= 1.5-0), rJava (>= 0.9-0)
+Depends: stream (>= 1.5-1), rJava (>= 0.9-0)
 Imports: graphics, stats, methods
 SystemRequirements: Java (>= 8)
 BugReports: https://github.com/mhahsler/streamMOA

NEWS.md

@@ -1,4 +1,4 @@
-#  Changes in version 1.2-3.1 (xx/xx/20)
+#  Changes in version 1.2-4 (05/09/22)
 
 * Removed registry
 * We use now roxygen2

R/DSC_ClusTree.R

@@ -40,8 +40,7 @@
 #' @references Philipp Kranen, Ira Assent, Corinna Baldauf, and Thomas Seidl.
 #' 2009. Self-Adaptive Anytime Stream Clustering. In Proceedings of the 2009
 #' Ninth IEEE International Conference on Data Mining (ICDM '09). IEEE Computer
-#' Society, Washington, DC, USA, 249-258. DOI=10.1109/ICDM.2009.47
-#' \url{http://dx.doi.org/10.1109/ICDM.2009.47}
+#' Society, Washington, DC, USA, 249-258. \doi{10.1109/ICDM.2009.47}
 #'
 #' Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T
 #' (2010). MOA: Massive Online Analysis, a Framework for Stream Classification

R/DSOutlier_MCOD.R

@@ -61,30 +61,28 @@ DSC_MCOD <- function(r=0.1,t=50,w=1000,recheck_outliers=TRUE) {
 #' @seealso \code{\link{DSOutlier}}, \code{\link{DSC}},
 #' \code{\link{DSC_Micro}}, \code{\link{DSC_MOA}}
 #' @references Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, and
-#' Manolopoulos Y (2016). "Efficient and flexible algorithms for monitoring
-#' distance-based outliers over data streams." In "Information systems", vol.
-#' 55, pp.
-#' 37-53.c("\\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi(\"#1\")}",
-#' "10.1109/ICDE.2011.5767923")\Sexpr{tools:::Rd_expr_doi("10.1109/ICDE.2011.5767923")}
+#' Manolopoulos Y (2016). Efficient and flexible algorithms for monitoring
+#' distance-based outliers over data streams. Information systems, vol.
+#' 55, pp. 37-53. \doi{10.1109/ICDE.2011.5767923}
 #' @examples
+#' set.seed(1234)
 #'
 #' # two-stage example
 #' stream <- DSD_Gaussians(k = 3, d = 2,
-#'             separation_type = "Mahalanobis", separation = 4,
+#'             separation_type = "Mahalanobis", separation = 2,
 #'             space_limit = c(0, 30), variance_limit = 0.8,
 #'             outliers = 10,
-#'             outlier_options = list(outlier_horizon = 1000))
+#'             outlier_options = list(outlier_horizon = 200))
 #'
-#' mic_c <- DSC_MCOD(r = 1, t = 10, w = 1000)
+#' mic_c <- DSC_MCOD(r = 2, t = 10, w = 200)
 #' mac_c <- DSC_Kmeans(3)
 #' c <- DSC_TwoStage(mic_c, mac_c)
 #'
-#' evaluate(c, stream, n = 1000, type = "macro",
+#' evaluate(c, stream, n = 200, type = "macro",
 #'   measure = c("crand","outlierjaccard"))
 #'
 #' reset_stream(stream)
-#' plot(c, stream, n = 1000, type = "all")
-#'
+#' plot(c, stream, n = 200, type = "all")
 #' @export DSOutlier_MCOD
 DSOutlier_MCOD <- DSC_MCOD