ssq(
  maxArrivals = Inf,
  seed = NA,
  interarrivalFcn = NULL,
  serviceFcn = NULL,
  interarrivalType = "M",
  serviceType = "M",
  maxTime = Inf,
  maxDepartures = Inf,
  maxInSystem = Inf,
  maxEventsPerSkyline = 15,
  saveAllStats = FALSE,
  saveInterarrivalTimes = FALSE,
  saveServiceTimes = FALSE,
  saveWaitTimes = FALSE,
  saveSojournTimes = FALSE,
  saveNumInQueue = FALSE,
  saveNumInSystem = FALSE,
  saveServerStatus = FALSE,
  showOutput = TRUE,
  animate = FALSE,
  show = NULL,
  showQueue = TRUE,
  showSkyline = NULL,
  showSkylineSystem = TRUE,
  showSkylineQueue = TRUE,
  showSkylineServer = TRUE,
  showTitle = TRUE,
  showProgress = TRUE,
  plotQueueFcn = defaultPlotSSQ,
  plotSkylineFcn = defaultPlotSkyline,
  jobImage = NA,
  plotDelay = NA,
  respectLayout = FALSE
)

# process 200 arrivals, R-provided seed (via NULL seed)
 ssq(200, NULL)

 ssq(maxArrivals = 200, seed = 54321)
 ssq(maxDepartures = 200, seed = 54321)
 ssq(maxTime = 100, seed = 54321)

 ############################################################################
 # example to show use of seed = NA (default) to rely on current state of generator
 output1 <- ssq(200, 8675309, showOutput = FALSE, saveAllStats = TRUE)
 output2 <- ssq(300,          showOutput = FALSE, saveAllStats = TRUE)
 set.seed(8675309)
 output3 <- ssq(200,          showOutput = FALSE, saveAllStats = TRUE)
 output4 <- ssq(300,          showOutput = FALSE, saveAllStats = TRUE)
 sum(output1$sojournTimes != output3$sojournTimes) # should be zero
 sum(output2$sojournTimes != output4$sojournTimes) # should be zero

 myArrFcn <- function() { vexp(1, rate = 1/4, stream = 1)  }  # mean is 4
 mySvcFcn <- function() { vgamma(1, shape = 1, rate = 0.3) }  # mean is 3.3

 output <- ssq(maxArrivals = 100, interarrivalFcn = myArrFcn, serviceFcn = mySvcFcn,
              saveAllStats = TRUE)
 mean(output$interarrivalTimes)
 mean(output$serviceTimes)
 meanTPS(output$numInQueueT, output$numInQueueN) # compute time-averaged num in queue
 meanTPS(output$serverStatusT, output$serverStatusN) # compute server utilization

 ############################################################################
 # example to show use of (simple) trace data for arrivals and service times;
 # ssq() will need one more interarrival (arrival) time than jobs processed
 #
 initTimes <- function() {
     arrivalTimes      <<- c(15, 47, 71, 111, 123, 152, 232, 245, 99999)
     interarrivalTimes <<- c(arrivalTimes[1], diff(arrivalTimes))
     serviceTimes      <<- c(43, 36, 34, 30, 38, 30, 31, 29)
 }

 getInterarr <- function() {
     nextInterarr <- interarrivalTimes[1]
     interarrivalTimes <<- interarrivalTimes[-1] # remove 1st element globally
     return(nextInterarr)
 }

 getService <- function() {
     nextService <- serviceTimes[1]
     serviceTimes <<- serviceTimes[-1]  # remove 1st element globally
     return(nextService)
 }

 initTimes()
 numJobs <- length(serviceTimes)
 output <- ssq(maxArrivals = numJobs, interarrivalFcn = getInterarr,
               serviceFcn = getService, saveAllStats = TRUE)
 mean(output$interarrivalTimes)
 mean(output$serviceTimes)


 ############################################################################
 # example to show use of (simple) trace data for arrivals and service times,
 # allowing for reuse (recycling) of trace data times

 initArrivalTimes <- function() {
   arrivalTimes      <<- c(15, 47, 71, 111, 123, 152, 232, 245)
   interarrivalTimes <<- c(arrivalTimes[1], diff(arrivalTimes))
 }

 initServiceTimes <- function() {
     serviceTimes      <<- c(43, 36, 34, 30, 38, 30, 31, 29)
 }

 getInterarr <- function() {
     if (length(interarrivalTimes) == 0)  initArrivalTimes()

     nextInterarr <- interarrivalTimes[1]
     interarrivalTimes <<- interarrivalTimes[-1] # remove 1st element globally
     return(nextInterarr)
 }

 getService <- function() {
     if (length(serviceTimes) == 0)  initServiceTimes()

     nextService <- serviceTimes[1]
     serviceTimes <<- serviceTimes[-1]  # remove 1st element globally
     return(nextService)
 }

 initArrivalTimes()
 initServiceTimes()
 output <- ssq(maxArrivals = 100, interarrivalFcn = getInterarr,
               serviceFcn = getService, saveAllStats = TRUE)
 mean(output$interarrivalTimes)
 mean(output$serviceTimes)

 ############################################################################
 # Testing with visualization

 ## Not run: 
 # Visualizing ssq with a set seed, infinite queue capacity, 20 arrivals,
 # interactive mode (default), showing skyline for all 3 attributes (default)
 ssq(seed = 1234, maxArrivals = 20, animate = TRUE)
 
## End(Not run)

 # Same as above, but jump to final queue visualization
 ssq(seed = 1234, maxArrivals = 20, animate = TRUE, plotDelay = 0)

 # Perform simulation again with finite queue of low capacity. Note same
 # variable generation but different outcomes due to rejection pathway
 ssq(seed = 1234, maxArrivals = 25, animate = TRUE, maxInSystem = 5, plotDelay = 0)

 # Using default distributions to make a default M/G/1 Queue
 ssq(seed = 1234, maxDepartures = 10, interarrivalType = "M", serviceType = "G", 
     animate = TRUE, plotDelay = 0)