## libraries needed
## sm, mvtnorm, akima
## make sure you have these installed

library(sm)
# gives the MI using the formulation of Moon et al. (1995)
source("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess3/MI/minf.r")
# MI confidence interval
source("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess3/MI/mi_confs.r")

#computes MI based on a Normal PDF for the joint and marginals in the
# MI formulation
source("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess3/MI/minf_normal.r")	

#computes the bootstrap confidence interval based on 
# Normal PDF, boxplots them and shows the  MI of the data
# based on kernel PDF
source("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess3/MI/mi_normal_confs.r")	

## Myboxplots
source("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess1/myboxplot.r")
source("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess1/myboxplot-stats.r")

## read data
test=matrix(scan("http://civil.colorado.edu/~balajir/CVEN5454/R-sessions/sess3/MI/after.txt"),ncol=2,byrow=T)

x=test[,1]
y=test[,2]

minf(x,y)		#gives the MI using the formulation of Moon et al. (1995)
minfconf(x,y)		#computes the bootstrap confidence intervals for MI annd correlation


minfnormal(x,y)		#gives the MI using Normal PDFs - Normal bivariate, and Normal marginal
minfnormalconf(x,y)		#computes the bootstrap confidence intervals for MI and correlation
# using Normal bivariate and Normal marginal PDFS