Function for determining the monotone part (eta.mu) of the local
significance levels for the key equation of the informative SCI algorithm.
The function creates dual graphs and rejects some of its hypotheses to
obtain the local significance levels.
weightsGTP(mu, g, weights, alpha, q, mu_0)Returns a numeric vector of dimension m (eta.mu) used for
solving the key equation of the informSCI algorithm. It contains the
local levels in mu divided by q^{max(mu-mu_0,0)} or divided by
adapted information weights (only if q[i]>0).
A real-valued vector (-Inf is also allowed) of dimension m
indicating which dual graph should be created and which null hypotheses
should be rejected. mu[i]>mu_0[i] iff the corresponding hypothesis is
rejected, \(1\leq i\leq m\).
A numeric square matrix of transition weights for the graphical test procedure.
A numeric vector of dimension m of initial weights for the graphical test procedure.
Overall level of the graphical test procedure.
A numeric vector of dimension m of information weights.
A numeric vector of dimension m of bounds of the null hypotheses.
m = number of hypotheses.
The function is not suitable if for all \(1\leq i\leq m\) it holds
q[i]==0 and mu[i]>mu_0[i].