Prerequisites:
Features in rows and samples in columns
make sure that data do not contain any zeros
To obtain plots make sure that graphics are enabled for example by using Xming
To run the software:
source(file="mswsd_resamp_publi.R") # this will install the necessary functions
to check for unbalanced regulation it will be analyzed whether total spectral areas are normally distributed (Shapiro-Wilk normality test)
use:
unbal_reg(my.data)
In case of unbalanced regulation you may want to perform normalizations without highly variable features. For this you have
to identify the amount of features to be excluded. This is based on resampling of mswsd values
To do the resampling of the mswsd values use newly installed function 
"resamp_mswsd(my.data)" 
whwere "my.data" contains a data matrix of your metabolite data without zeros with features in rows and samples in 
columns.
Results of the resampling approach will be given as a plot in PDF-formate ("resamp_mswsd.pdf").
From this plot identify manually the percentage of features where the mswsd values approach a stable value.
This value may then be used for subsequent data normalization for example 80 percent.
Here it is important that you do not reduce the amout of features too much so that in an extrem case only noise features remain.
Then run normalization with 
norm.my.data<-norm_unbal(my.data,80,"VSN")
The first argument is your data, the second the amount of features to be used and the third the normalization to be applied. Available 
normalizations are linear baseline normalization based on mean values ("LBME"), linear baseline normalization based on median values
(LBMD"), probabilistic quotient normalization ("PQN") and variance stabilization normalization ("VSN") 
"norm.my.data" contains now the normalized data