Changed description

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: EventDetectR
-Version: 0.3.4
+Version: 0.3.6
 Title: Event Detection Framework
 Description: Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.
 Authors@R: 
   c(
   person("Margarita", "Rebolledo", email="[email protected]", role=c("aut")),
-  person("Sowmya", "Chandrasekaran", email="[email protected]", role=c("aut")),
-  person("Frederik", "Rehbach", email="[email protected]", role=c("aut", "cre")),
+  person("Sowmya", "Chandrasekaran", email="[email protected]", role=c("aut", "cre")),
+  person("Frederik", "Rehbach", email="[email protected]", role=c("aut")),
   person("Steffen", "Moritz", email="[email protected]", role=c("aut"), comment = c(ORCID = "0000-0002-0085-1804"))
   )
-Maintainer: Sowmya Chandrasekaran<[email protected]>
+Encoding: UTF-8
 LazyData: yes
 Type: Package
 ByteCompile: TRUE
@@ -18,7 +18,8 @@ URL: https://github.com/frehbach/EventDetectR
 Repository: CRAN
 Depends:
     R (>= 3.1.0)
-Imports: imputeTS, forecast, ggplot2, gridExtra, neuralnet
+Imports: imputeTS, forecast, ggplot2, gridExtra, neuralnet, Rdpack
+RdMacros: Rdpack
 Suggests: testthat, utils, caret, e1071
 License: GPL-3
-RoxygenNote: 6.1.1
+RoxygenNote: 7.1.1

NAMESPACE

@@ -16,3 +16,4 @@ import(imputeTS)
 import(neuralnet)
 import(stats)
 import(utils)
+importFrom(Rdpack,reprompt)

R/EventDetectR-package.R

@@ -1,14 +1,16 @@
 #' @title EventDetectR-package description
 #' @description
-#' The EventDetectR package is a modular event detection system
+#' Detect events/ anomalies in time-series data.
+
 #'
 #' @details The EventDetectR package enables detection of events/ anomalies in multivariate time-series data. It combines multiple well-known R packages like 'forecast, 'neuralnet' to deliver an easily configurable tool for event detection.
 #'
 #' @name EventDetectR-package
-#'
-#'
 #' @docType package
 #' @import stats
 #' @import utils
+#' @importFrom Rdpack reprompt
+#' @references
+#' \insertRef{Chan20a}{EventDetectR}\cr
 if(getRversion() >= "2.15.1")  utils::globalVariables(c("x","y"))
 NULL

R/detectEvents.R

@@ -138,7 +138,7 @@ detectEvents <- function(x,
         }else{
             modelingData <- x[1:(index + windowSize),,drop=FALSE]
             eventPositions <- which(edModel$eventHistory)
-# Added by Sowmya to check if eventPositions exists
+# Added to check if eventPositions exists
 if(length(eventPositions>0))
 {
             modelingData <- modelingData[-eventPositions, , drop = FALSE]

R/getDefaultControlLists.R

@@ -41,6 +41,6 @@ getDefaultPostControl <- function() {
     defaultControlList <- list(
         nStandardDeviationseventThreshold = 2,
         eventThreshold = .7,
-        bedWindowSize = 30
+        bedWindowSize = 10
     )
 }

R/model_NeuralNetwork.R

@@ -2,7 +2,7 @@
 #' Fitting Neuralnet Models
 #' @details Multivariate Neuralnetwork model is formulated for each of the parameter using its own lagged values and rest of all parameters involved. For instance, considering 3 parameters 'p1', 'p2', 'p3', the value of 'p1' at time step 'i' is calclated as \cr
 #' \code{p1[i] = p2[i] + p3[i] + p1[i-1]} \cr
-#'
+#' It is recommended to activate the normalization of the data.
 #' @param x data
 #' @param control control list with settings
 #' @return fitted multivariate neural network model

README.md

@@ -17,7 +17,9 @@ require(devtools)
 install_github("frehbach/EventDetectR")
 ```
 
-Soon the package will also be available on CRAN.
+The package is available on CRAN and can be installed:
+
+install.packages('EventDetectR')
 
 ## Usage
 The main function of the EventDetectR package is:
@@ -69,7 +71,7 @@ getDefaultModelControl()
 getDefaultPostControl()
 ```
 
-The event classification itself is shown in the graph below. #TODO make this graph look nice
+The event classification itself is shown in the graph below. 
 
 ![Alt text](doc/Ver2_windowimage.png?raw=true "detectEvents.R")
 
@@ -97,7 +99,7 @@ Since it was not specified on the function call the event duration defaults to 1
 -------------
 
 ## Graphical User Interface
-A graphical user interface (GUI) for the EventDetectR package is currently in development. 
+A graphical user interface (GUI) for the EventDetectR package is also available. 
 Check out the most recent status at: [EventDetectGUI](https://github.com/frehbach/EventDetectGUI)
 
 -------------

inst/CITATION

@@ -1,11 +1,13 @@
-year <- sub("-.*", "", meta$Date)
-vers <- paste("R package version", meta$Version)
+year <- 2019
+vers <- paste("R package version", year)
 
 citHeader("To cite the EventDetectR package, use:")
 
 bibentry(bibtype = "Manual",
   title = "EventDetectR",
-  author = personList(as.person("Steffen Moritz"), as.person("Frederik Rehbach")),
+  author = personList(as.person("Sowmya Chandrasekaran"),as.person("Steffen Moritz"), as.person("Margarita Rebolledo"),as.person("Frederik Rehbach")),
   year = year,
   note = vers,
+  organization = "Institute for Data Science, Engineering, and Analytics, Technische Hochschule Koeln",
+  address = "Steinmüllerallee 1, 51643 Gummersbach",
   url = "https://CRAN.R-project.org/package=EventDetectR")

inst/REFERENCES.bib

@@ -0,0 +1,9 @@
+@techreport{Chan20a,
+  author    = {Sowmya Chandrasekaran and Margarita Rebolledo and Thomas Bartz-Beielstein},
+  title     = {EventDetectR – An Open-Source Event Detection System},
+  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-9232},
+  abstract  = {EventDetectR: An efficient Event Detection System (EDS) capable of detecting unexpected water quality conditions. This approach uses multiple algorithms to model the relationship between various multivariate water quality signals. Then the residuals of the models were utilized in constructing the event detection algorithm, which provides a continuous measure of the probability of an event at every time step. The proposed framework was tested for water contamination events with industrial data from automated water quality sensors. The results showed that the framework is reliable with better performance and is highly suitable for event detection.},
+  language  = {en},
+  institution={Institute for Data Science, Engineering, and Analytics, TH K\"oln, Steinmüllerallee 1, 51643 Gummersbach, Germany},
+  year={2020}
+}

tests/testthat/test-eventClassification.R

@@ -88,15 +88,6 @@ test_that("neural network prediction works",
               expect_true(!any(p2$Event))
 
 
-              #Check that normalization is really deactivated if setting says so
-              x <- stationBData[100:200,-1]
-              m <- buildEDModel(x, dataPreparationControl = list(useNormalization = F),buildModelAlgo = "NeuralNetwork",ignoreVarianceWarning = TRUE)
-              excludedVariable <- length(m$excludedVariables)
-              newDataReal <- stationBData[201:220,-1]
-              p <- predict.NeuralNetwork(m,newDataReal)
-              expect_equal(nrow(p$predictions),nrow(newDataReal))
-              expect_equal(ncol(p$predictions),(ncol(stationBData)-1-excludedVariable))
-
           })