Update for forecast v9

NAMESPACE

@@ -1,6 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(plot,edObject)
+S3method(predict,NeuralNetwork)
+S3method(predict,UnivariateForecast)
 S3method(print,edObject)
 export(buildEDModel)
 export(detectEvents)

R/detectEvents.R

@@ -168,7 +168,7 @@ if(length(eventPositions>0))
 }
 
 model_name <- function(x, ...){
-    sub("forecast_", "", class(x)[1])
+    sub("forecast_", "", tail(class(x),1))
 }
 
 #' Print an Event Detection Object

R/model_Forecast.R

@@ -62,6 +62,7 @@ model_UnivariateForecast <- function(x, strName, control){
 #' @return predicted value
 #' @import stats
 #' @keywords internal
+#' @exportS3Method predict UnivariateForecast
 predict.UnivariateForecast <- function(object,newData = NULL, ...){
     ## How many points shall be predicted into the future? Default = 10
     if(!is.null(newData)){

R/model_NeuralNetwork.R

@@ -58,7 +58,7 @@ control$threshold <- NULL
 #'
 
 
-
+#' @exportS3Method predict NeuralNetwork
 predict.NeuralNetwork <- function(object,newData = NULL, ...){
     ## How many points shall be predicted into the future? Default = 10
     if(!is.null(newData)){

tests/testthat/test-eventClassification.R

@@ -51,11 +51,11 @@ test_that("print works",
                                  buildModelAlgo = "ForecastArima")
 
               printRes <- capture.output(print(ed))
-              expect_equal(printRes[1], "Event Detection Object with 11 ARIMA submodels")
+              expect_equal(printRes[1], "Event Detection Object with 11 Arima submodels")
 
               ed$modelList <- ed$modelList[1]
               printRes <- capture.output(print(ed))
-              expect_equal(printRes[1], "Event Detection Object with 1 ARIMA submodel")
+              expect_equal(printRes[1], "Event Detection Object with 1 Arima submodel")
 
               ed$modelList <- NULL
               printRes <- capture.output(print(ed))
@@ -89,5 +89,3 @@ test_that("neural network prediction works",
 
 
           })
-
-