+io <- generateSampleIO(n=10000, p=1/2, beta=matrix(c(1,0,0,1),ncol=2), b=c(0,0), link="probit")
+
+n is the total number of samples (lines in X, number of elements in Y)
+p is a vector of proportions, of size d-1 (because the last proportion is deduced from
+ the others: p elements sums to 1) [TODO: omega or p?]
+beta is the matrix of linear coefficients, as written above in the model.
+b is the vector of intercepts (as in linear regression, and as in the model above)
+link can be either "logit" or "probit", as mentioned earlier.
+
+This function outputs a list containing in particular the matrices X and Y, allowing to
+use the other functions (which all require either these, or the moments).
+
+TODO: computeMu(), explain input/output
+
+### Estimation of other parameters
+
+TODO: just run optimParams$run(...)
+
+### Monte-Carlo and bootstrap
+
+TODO: show example comparison with flexmix, show plots.