1) EITHER:  Download the tar file R.all.tar. To untar the file under UNIX/LINUX, 
type at the UNIX/LINUX prompt: 
            tar -xvf R.all.tar             
  (this creates the directory "Rgamcode" containing all the code and data)


   ALTERNATIVELY: download each function one by one into the desired directory. 
   This will work under any platform. 


2) Start R from working directory Rgamcode. Type
	> source("startup.func")
	> startup.func()

This reads all relevant functions into R.

Read in the example data set with:
	> cb <- read.table("cb", header=T)

All objects can now be removed from the working directory if required.


3) Demonstration analysis on the data set cb. 
Enter the following commands:

 > library(mgcv)     
 > # (needed every session to attach the mgcv library)  
 > # If you get an error, read the instructions at 
 > # http://www.stat.auckland.ac.nz/~fewster/gams/R/
 > # about installing the library mgcv. 

 > indcb <- indsp.func(cb, c(4, 7, 10, 15, 20, 33))
 > # (calculates index curve for a variety of df)

 > cb.bootind.119.10 <- outer.boot.func(cb, 10, 119, "cb.bootind.119.10")
 > # (this is the bootstrapping: might take a minute or two for 119
 > # replicates)

 > sp.plot("cb", 10, cb.bootind.119.10, 1, 6, 1, conf=0.95)

The other functions were all used invisibly in the demonstration.