diff --git a/server.r b/server.r
deleted file mode 100644
index 38ab3810a4f0dcc26fc354ed179c3f018047ab9c..0000000000000000000000000000000000000000
--- a/server.r
+++ /dev/null
@@ -1,343 +0,0 @@
-# Define a server for the Shiny app
-function(input, output,session) {
-
-
-  # Create values which will contain the changing values
-  myvalues <- reactiveValues()
-  
- values <- reactiveValues(tx_star_b=c()) 
-values2 <- reactiveValues(sigmax_star_b=c()) 
-values3 <- reactiveValues(tx_star_bMINUStheta_fn=c()) 
-values4 <- reactiveValues(u_star_b=c()) 
-values5 <- reactiveValues(xbar=c()) 
-
-
-  
-  
-  # Create an empty dataframe (within values) which will contain the generated data
-  myvalues$df <- data.frame(Low= integer(0),Up = numeric(0), stringsAsFactors = FALSE)
-
-
-
-
-
- res <- reactive({
-
-mu=input$mu
-n=input$n
-B=input$B
-N=20 
-
-b=input$bb
-
-
-xbar		= NULL
-tx_star_b=NULL
-sigmax_star_b=NULL
-u_star_b=NULL
-tx_star_bMINUStheta_fn=NULL
-
-
-
-
-  
-nrow = N; ncol = N;lambda=1/mu; p.col = c("deepskyblue", "red"); p.cex = c(1, 3)  
-set.seed(2018+as.numeric(input$submit_model))	
-population <- rexp(nrow*ncol,rate=lambda)
-population <- matrix(population,nrow,ncol)
-
-par(mar=c(0.5, 0.5, 0.5, 0.5))
-x = cbind(rep(1:ncol, nrow), gl(nrow, ncol))
-populationIndex = cbind(rep(1:ncol, nrow), gl(nrow, ncol))
-
-
-set.seed(2018+as.numeric(input$submit_model))
-myx=x[sample(nrow * ncol, n), ]
-myxx=population[myx]		
-
-
-
-set.seed(2018+as.numeric(input$submit_model))			
-originalsampleIndex=x[sample(nrow * ncol, n), ]
-
-
-for (b in 1:B){
-
-set.seed(2018+b+(B*n*as.numeric(input$submit_model)))	
-BootSampleIndex=sample(1:n, n,replace = T)
-mypcex=rep(p.cex[2],length(originalsampleIndex))	
-mypcex2=seq(0,100,by=2)
-
-freq_vec=rep(NA,length(BootSampleIndex))	
-mypcex3=rep(NA,length(BootSampleIndex))	
-for (j in 1:length(unique(BootSampleIndex))){
-mylength=length(freq_vec[which(BootSampleIndex==unique(BootSampleIndex)[j])])
-freq_vec[which(BootSampleIndex==unique(BootSampleIndex)[j])]=1:mylength
-mypcex3[which(BootSampleIndex==unique(BootSampleIndex)[j])]=mypcex2[1:mylength]
-}
-
-mypcex4=mypcex+mypcex3
-mypcex4
-
-BootSampleIndexMatrix=originalsampleIndex[BootSampleIndex, ]
-BootSample=population[BootSampleIndexMatrix]		
- 
-  # cat(file=stderr(), " ", as.numeric(guessInput5()), " ", "\n")	    
-
-	orig.mean		= mean(population[originalsampleIndex])
-	orig.var		= var(population[originalsampleIndex])*((n-1)/n)
-	
-# cat(file=stderr(), " ", as.numeric(orig.mean), " ", "\n")	
-
-tx_star_b <- c(tx_star_b,mean(population[BootSampleIndexMatrix]))
-sigmax_star_b=c(sigmax_star_b,sqrt(var(population[BootSampleIndexMatrix])*(n-1)/n))
-tx_star_bMINUStheta_fn=c(tx_star_bMINUStheta_fn,mean(population[BootSampleIndexMatrix])-mean(population[originalsampleIndex]))
-u_star_b=c(u_star_b,sqrt(n)*(mean(population[BootSampleIndexMatrix])-mean(population[originalsampleIndex]))/sqrt(var(population[BootSampleIndexMatrix])*(n-1)/n))
-xbar=c(xbar, as.numeric(mean(BootSample)))
-
-}
-
- cat(file=stderr(), " ", as.numeric(tx_star_b), " ", "\n")	
-
- 	 
-CIboot=c(orig.mean-quantile(tx_star_bMINUStheta_fn,1-0.05/2),orig.mean-quantile(tx_star_bMINUStheta_fn,0.05/2))
-CIboot2=c(2*orig.mean-quantile(tx_star_b,1-0.05/2),2*orig.mean-quantile(tx_star_b,0.05/2))
-CItboot=c(orig.mean-sqrt(orig.var)*quantile(u_star_b,1-0.05/2)/sqrt(n),orig.mean-sqrt(orig.var)*quantile(u_star_b,0.05/2)/sqrt(n))
-CIpboot=c(orig.mean+quantile(tx_star_bMINUStheta_fn,0.05/2),orig.mean+quantile(tx_star_bMINUStheta_fn,1-0.05/2))
-CIasymp=c(orig.mean-qnorm(1-0.05/2,mean=0,sd=sqrt(orig.var))/sqrt(n),orig.mean-qnorm(0.05/2,mean=0,sd=sqrt(orig.var))/sqrt(n))
-	 
-	 
-	return(c(as.numeric(CIboot[1]),as.numeric(CIboot[2]),as.numeric(b))) 
-  })  
-   
- 
- 
-newEntry <- observe({
-    if(input$submit_model >= 0) {
-      isolate(
-        myvalues$df[nrow(myvalues$df) + 1,] <- c(isolate(res())[1],isolate(res())[2])    
-        )}
-  })  
-  
-  
-  
-output$res_table <- renderTable({
-	myvalues$df
-  })
- 
- 
-observeEvent( input$refresh, { 
-       updateSliderInput(session, "bb",  label="", value=1, min=1, max=input$B, step=1)
-  set.seed(2018+as.numeric(input$submit_model))    
-tx_star_b=c() 
-sigmax_star_b=c() 
-tx_star_bMINUStheta_fn=c() 
-u_star_b=c()
-xbar=c() 
-    })  
- 
-observeEvent( input$submit_model, {
-tx_star_b=c() 
-sigmax_star_b=c() 
-tx_star_bMINUStheta_fn=c() 
-u_star_b=c()
-xbar=c() 
-    })   
- 
- 
- 
-
-  # Fill in the spot we created for a plot
-  output$Plot <- renderPlot({
-
-
-  
-par(mfrow=c(4,2))
-
-mu=input$mu
-n=input$n
-B=input$B
-b=input$bb
-N=20 
-
-xbar		= NULL
-tx_star_b=NULL
-sigmax_star_b=NULL
-u_star_b=NULL
-tx_star_bMINUStheta_fn=NULL
-
-  
-nrow = N; ncol = N;lambda=1/mu; p.col = c("deepskyblue", "red"); p.cex = c(1, 3)  
-set.seed(2018+as.numeric(input$submit_model))	
-population <- rexp(nrow*ncol,rate=lambda)
-population <- matrix(population,nrow,ncol)
-
-par(mar=c(0.5, 0.5, 0.5, 0.5))
-x = cbind(rep(1:ncol, nrow), gl(nrow, ncol))
-populationIndex = cbind(rep(1:ncol, nrow), gl(nrow, ncol))
-
-set.seed(2018+as.numeric(input$submit_model))
-myx=x[sample(nrow * ncol, n), ]
-myxx=population[myx]		
-
-mycol<-NULL
-mycol<-c(mycol,"red")
-mycol[]<-"gray"
-mycol[1]<-"red"
-
-plot(x, pch = 19, col = "deepskyblue", cex = p.cex[1], axes = FALSE, ann = FALSE, xlab = "", ylab = "",bty="n")
-points(myx, col = p.col[1], pch = 19, cex = p.cex[1])	
-points(myx, col = p.col[2], cex = p.cex[2])
-
-
-
-set.seed(2018+as.numeric(input$submit_model))			
-originalsampleIndex=x[sample(nrow * ncol, n), ]
-
-
-	orig.mean		= mean(population[originalsampleIndex])
-
-
-par(mar=c(0,0,0,0))
-
-plot(x, pch = 19, col = "white", cex = p.cex[1], axes = FALSE, ann = FALSE, xlab = "", ylab = "",bty="n")
-points(originalsampleIndex, col = p.col[1], pch = 19, cex = p.cex[1])	
-
-set.seed(2018+b+(B*n*as.numeric(input$submit_model)))
-BootSampleIndex=sample(1:n, n,replace = T)
-mypcex=rep(p.cex[2],length(originalsampleIndex))	
-mypcex2=seq(0,100,by=2)
-
-freq_vec=rep(NA,length(BootSampleIndex))	
-mypcex3=rep(NA,length(BootSampleIndex))	
-for (j in 1:length(unique(BootSampleIndex))){
-mylength=length(freq_vec[which(BootSampleIndex==unique(BootSampleIndex)[j])])
-freq_vec[which(BootSampleIndex==unique(BootSampleIndex)[j])]=1:mylength
-mypcex3[which(BootSampleIndex==unique(BootSampleIndex)[j])]=mypcex2[1:mylength]
-}
-
-mypcex4=mypcex+mypcex3
-mypcex4
-
-BootSampleIndexMatrix=originalsampleIndex[BootSampleIndex, ]
-BootSample=population[BootSampleIndexMatrix]		
-points(BootSampleIndexMatrix, col = p.col[2], cex = mypcex4)		
-
-# xb[b,]=BootSample
-
-
-guessInput <- reactive({
-    isolate({
-      values$tx_star_b <- c(values$tx_star_b, as.numeric(mean(population[BootSampleIndexMatrix])))
-      return(values$tx_star_b)   
-    })
-  })
-
-guessInput2 <- reactive({
-    isolate({
-      values2$sigmax_star_b <- c(values2$sigmax_star_b, as.numeric(sqrt(var(population[BootSampleIndexMatrix])*(n-1)/n)))
-      return(values2$sigmax_star_b)   
-    })
-  })  
-  
-guessInput3 <- reactive({
-    isolate({
-      values3$tx_star_bMINUStheta_fn <- c(values3$tx_star_bMINUStheta_fn, as.numeric(mean(population[BootSampleIndexMatrix])-mean(population[originalsampleIndex])))
-      return(values3$tx_star_bMINUStheta_fn)   
-    })
-  })  
-
-guessInput4 <- reactive({
-    isolate({
-      values4$u_star_b <- c(values4$u_star_b, as.numeric(sqrt(n)*(mean(population[BootSampleIndexMatrix])-mean(population[originalsampleIndex]))/sqrt(var(population[BootSampleIndexMatrix])*(n-1)/n)))
-      return(values4$u_star_b)   
-    })
-  })  
-      
-	
- # cat(file=stderr(), " ", as.numeric(guessInput()), " ", "\n")	
-   # cat(file=stderr(), " ", as.numeric(guessInput2()), " ", "\n")	
-    # cat(file=stderr(), " ", as.numeric(guessInput3()), " ", "\n")	
-	 # cat(file=stderr(), " ", as.numeric(guessInput4()), " ", "\n")	
-  
-tx_star_b <- c(tx_star_b,mean(population[BootSampleIndexMatrix]))
-sigmax_star_b=c(sigmax_star_b,sqrt(var(population[BootSampleIndexMatrix])*(n-1)/n))
-tx_star_bMINUStheta_fn=c(tx_star_bMINUStheta_fn,mean(population[BootSampleIndexMatrix])-mean(population[originalsampleIndex]))
-u_star_b=c(u_star_b,sqrt(n)*(mean(population[BootSampleIndexMatrix])-mean(population[originalsampleIndex]))/sqrt(var(population[BootSampleIndexMatrix])*(n-1)/n))
-
-mycol2<-c(rep("gray",length(as.numeric(guessInput()))-1),"red")
-
-plot(0,type="n",main="",ylab='',yaxt='n',xaxt='n',bty='n')
-plot(0,type="n",main="",ylab='',yaxt='n',xaxt='n',bty='n')
-
- 
-par(mar=c(2, 3, 0, 1),mgp=c(5,1,0))
-hist(as.numeric(guessInput()),freq = TRUE,xlim=c(0,mu+100),main="",ylab='',xaxt='n',bty='n',cex.axis=2)	
-points(as.numeric(guessInput()),rep(0,length(as.numeric(guessInput()))),cex.lab=2.2,xlim=c(0,mu+100),
-xlab='',ylab='',pch=17,col=mycol2,cex=1.4, xaxt='n',bty='n')
-axis(1, seq(0,mu+100,length=5), cex.axis=2, tck=-.01)
-mtext("Frequency",at=-100,side=1,line =-22,cex=2,las=2)
-	
-		
-points(50,0,cex.lab=2.2,xlim=c(1,3),ylim=c(0,150),xlab='',ylab='',pch=15,col="blue",cex=1.4, yaxt='n', xaxt='n',bty='n')	
-points(mean(population[originalsampleIndex]),0,cex.lab=2.2,xlim=c(1,3),xlab='',ylab='',pch=18,col="magenta",cex=1.8, yaxt='n', xaxt='n',bty='n')		
-points(mean(as.numeric(guessInput())),0,cex.lab=2.2,xlim=c(1,3),xlab='',ylab='',pch=19,col="green",cex=1.4, yaxt='n', xaxt='n',bty='n')
-
-CIboot=c(orig.mean-quantile(as.numeric(guessInput3()),1-0.05/2),orig.mean-quantile(as.numeric(guessInput3()),0.05/2))
-
-
-
-plot(0,type="n",main="",ylab='',yaxt='n',xaxt='n',bty='n')
-legend("center",legend=c(
-as.expression(bquote(mu*"="*.(50))),
-as.expression(bquote(theta*"("*F[n]*")"*"="*.(format(mean(population[originalsampleIndex]), digits = 0)))),
-as.expression(bquote(T({{chi^"*("}^.(format(b, digits = 0))}^")")*"="*.(format(mean(population[BootSampleIndexMatrix]), digits = 0)))),
-as.expression(bquote(T(chi^"*")*"="*.(format(mean(as.numeric(guessInput())), digits = 0)))),
-paste("[",round(CIboot[1],2)," ; ",round(CIboot[2],2), "]",sep="") 
-),col=c("blue","magenta","red","green"),pch=c(15,18,17,19,NA),bty="n",cex=1.8)	
-
-
-
-}) 
- 
- 
-observeEvent( input$refresh, { 
-       updateSliderInput(session, "bb",  label="", value=1, min=1, max=input$B, step=1)
-  set.seed(2018+as.numeric(input$submit_model))    
- values$tx_star_b=c() 
-values2$sigmax_star_b=c() 
-values3$tx_star_bMINUStheta_fn=c() 
-values4$u_star_b=c()
-values5$xbar=c() 
-    })  
- 
-observeEvent( input$submit_model, {
- values$tx_star_b=c() 
-values2$sigmax_star_b=c() 
-values3$tx_star_bMINUStheta_fn=c() 
-values4$u_star_b=c()
-values5$xbar=c() 
-    })   
- 
-observeEvent( input$n, {
- values$tx_star_b=c() 
-values2$sigmax_star_b=c() 
-values3$tx_star_bMINUStheta_fn=c() 
-values4$u_star_b=c()
-values5$xbar=c() 
-    })   
-  
- 
-observeEvent( input$B, { 
-       updateSliderInput(session, "bb",  label="", value=1, min=1, max=input$B, step=1)
-	    values$tx_star_b=c() 
-values2$sigmax_star_b=c() 
-values3$tx_star_bMINUStheta_fn=c() 
-values4$u_star_b=c()
-values5$xbar=c() 
-    })   
-}
-
-
-
-