lassosum2
snp_lassosum2( corr, df_beta, delta = signif(seq_log(0.001, 3, 6), 1), nlambda = 20, lambda.min.ratio = 0.01, dfmax = 2e+05, maxiter = 500, tol = 1e05, ncores = 1 )
corr  Sparse correlation matrix as an SFBM.
If 

df_beta  A data frame with 3 columns:

delta  Vector of shrinkage parameters to try (L2regularization).
Default is 
nlambda  Number of different lambdas to try (L1regularization).
Default is 
lambda.min.ratio  Ratio between last and first lambdas to try.
Default is 
dfmax  Maximum number of nonzero effects in the model.
Default is 
maxiter  Maximum number of iterations before convergence.
Default is 
tol  Tolerance parameter for assessing convergence.
Default is 
ncores  Number of cores used. Default doesn't use parallelism. You may use nb_cores. 
A matrix of effect sizes, one vector (column) for each row in
attr(<res>, "grid_param")
. Missing values are returned when strong
divergence is detected.