Construct the data-driven grid for the regularization parameters used for the Lasso estimator
computeGridLambda(
phiInit,
rhoInit,
piInit,
gamInit,
X,
Y,
gamma,
mini,
maxi,
eps,
fast
)value for phi
for rho
for pi
value for gamma
matrix of covariates (of size n*p)
matrix of responses (of size n*m)
power of weights in the penalty
minimum number of iterations in EM algorithm
maximum number of iterations in EM algorithm
threshold to stop EM algorithm
boolean to enable or not the C function call
the grid of regularization parameters for the Lasso estimator. The output is a vector with nonnegative values that are relevant to be considered as regularization parameter as they are equivalent to a 0 in the regression parameter.