sample_HierarchicalModel: Sample from Hierarchical Model with given Row and Column Sums
In systemicrisk: A Toolbox for Systemic Risk

Sample from Hierarchical Model with given Row and Column Sums

sample_HierarchicalModel(l, a, L_fixed = NA, model, nsamples = 10000,
  thin = choosethin(l = l, a = a, L_fixed = L_fixed, model = model,
  matrpertheta = matrpertheta, silent = silent), burnin = NA,
  matrpertheta = length(l)^2, silent = FALSE,
  tol = .Machine$double.eps^0.25)

`l`	observed row sum
`a`	observerd column sum
`L_fixed`	Matrix containing known values of L, where NA signifies that an element is not known. If `L_fixed` equates to `NA` (the default) then no values are assumed to be known.
`model`	Underlying model for p and lambda.
`nsamples`	number of samples to return.
`thin`	how many updates of theta to perform before outputting a sample.
`burnin`	number of iterations for the burnin. Defaults to 5 of the steps in the sampling part.
`matrpertheta`	number of matrix updates per update of theta.
`silent`	(default FALSE) suppress all output (including progress bars).
`tol`	tolerance used in checks for equality. Defaults to `.Machine$double.eps^0.25`.

n <- 10
m <- Model.Indep.p.lambda(Model.p.BetaPrior(n),
                          Model.lambda.GammaPrior(n,scale=1e-1))
x <- genL(m)
l <- rowSums(x$L)
a <- colSums(x$L)
## Not run: 
res <- sample_HierarchicalModel(l,a,model=m)

## End(Not run)
# fixing one values
L_fixed <- matrix(NA,ncol=n,nrow=n)
L_fixed[1,2:5] <- x$L[1,2:5]
## Not run: 
res <- sample_HierarchicalModel(l,a,model=m,L_fixed=L_fixed,
                                nsamples=1e2)
sapply(res$L,function(x)x[1,2:5])

## End(Not run)