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mc.int.ex<-function(x.range=c(-3,3),int=c(-1.96,1.96),n=10000,plot=T,
  plot.pts=T,plot.poly=T,cols=list('black','green','blue'),round.val=2,val=F){
	
	require(scales)
	
	#create data for model fit
	x.fit<-seq(x.range[1],x.range[2],length=n)
	y.fit<-dnorm(x.fit)
	dat.fit<-data.frame(x.fit,y.fit)
	

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if (!require(ggplot2) | !require(scales) | !require(gridExtra) | !require(shiny)) {
  stop("This app requires the shiny, ggplot2, scales, and gridExtra packages. To install, run 'install.packages(\"name\")'.\n")
}

# Define server logic required to generate and plot a random distribution
shinyServer(function(input, output) {

  output$intPlot <- renderPlot({
    require(ggplot2)
    require(scales)

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column.int<-function(int.fun='dnorm(x)',num.poly=20,from.x=-1.96,to.x=1.96,plot.cum=F){
	
	if (!require(ggplot2) | !require(scales) | !require(gridExtra)) {
	  stop("This app requires the ggplot2, scales, and gridExtra packages. To install, run 'install.packages(\"name\")'.\n")
	}

	if(from.x>to.x) stop('Starting x must be less than ending x')
	
	if(is.character(int.fun)) int.fun<-eval(parse(text=paste('function(x)',int.fun)))
	

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SiteName  Year	Restoration	Reference	ObserverNames	Precipitation	Temperature
IGH	2005	3	3	Tyler_Amanda	0	48
Kelly	2005	4	2	Patrick_Chelsea	0	48
Carlton	2005	2	3	David_Megan	0	48
IGH	2006	9	6	Tyler_Amanda	0	52
Kelly	2006	9	1	David_Megan	0	52
Carlton	2006	7	3	Patrick_Chelsea	0	52
IGH	2007	12	7	David_Megan	12	41
Kelly	2007	2	18	Jeremy_Lucy	12	41
Carlton	2007	11	2	Patrick_Chelsea	12	41

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\documentclass[a4paper,12pt]{article} %document class declaration (article, report, book, etc) has to go first
\usepackage{times} %package imports times new roman font
\usepackage[margin=1in]{geometry} %change margins to 1in

\begin{document}
\renewcommand{\rmdefault}{ptm}

\setlength{\parindent}{0mm} %set indentation at start of new paragraphs
\setlength{\parskip}{0mm} %set white space between paragraphs

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shinyServer(function(input, output) {
 
  myData <- reactive({
   read.table(
    input$file$datapath,
    sep='\t',
 	  header=T,
 	  stringsAsFactors=F
    ) 
   })

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gar.fun<-function(out.var,mod.in,bar.plot=T,struct=NULL,x.lab=NULL,
                  y.lab=NULL, wts.only = F){
  
  require(ggplot2)
  require(plyr)
  
  # function works with neural networks from neuralnet, nnet, and RSNNS package
  # manual input vector of weights also okay
  
  #sanity checks

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plot.area<-function(x,col=NULL,horiz=F,prop=T,stp.ln=T,grp.ln=T,axs.cex=1,axs.lab=T,lab.cex=1,
                    names=c('Group','Step','Value'),...){
  
  #sort out color fector  
  if(!is.null(col)){
    if(sum(col %in% colors()) != length(col)) stop('col vector must be in "colors()"')
    col<-colorRampPalette(col)(ncol(x))
  }
  else col<-colorRampPalette(c('lightblue','green'))(ncol(x))
  

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#credit http://stackoverflow.com/questions/13649473/add-a-common-legend-for-combined-ggplots

require(ggplot2)
require(reshape)
require(gridExtra)

p.dat<-data.frame(step=row.names(grp.dat),grp.dat,stringsAsFactors=F)
p.dat<-melt(p.dat,id='step')
p.dat$step<-as.numeric(p.dat$step)

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lek.fun<-function(mod.in,var.sens=NULL,resp.name=NULL,exp.in=NULL,steps=100,split.vals=seq(0,1,by=0.2),val.out=F){
  
  require(ggplot2)
  require(reshape)
  
  ##
  #sort out exp and resp names based on object type of call to mod.in
  #get matrix for exp vars
  
  #for nnet