delete useless bit
Include a loop in lasso_regression_K_fixed to refine lambda till one find the right number of shifts.
Do not do the (costly) robust regression to find alpha if alpha is known.
Succedane to handle NAs in lm in laso_regression_K_fixed
Use quadrupen instead of glmnet.
Check that selected variables define a full rank matrix.
Change working dirrectory.
Fix segmentation by lasso regression (former error of indexation)
min.ratio = 0.0001 instead of 0
set seeds
Minor syntax fixes and loading quadrupen
Fixes #4
When doing a cholesky decomposition, the intercept is no more an intersept !
beta_0 is multiplied by L (if (LL'=Sigma).
Take this into account for the lm part of the gauss lasso, using lm.fit (instead of lm)
Fixes #6
Do the (hopefully not necessary) transition from K+1 shifts to K before the gauss lasso.
If use.sigma in lasso initialization, include the intercept in matrix T (column of ones at the end) so that it is correctly taken into account in gauss lasso.
On some exemples, improves greatly likelihood of initialization.
Compute log likelihood of the final param estimation
Separate gauss lasso in a function.
Project on the orthogonal of the root predictor before lasso.
Un-parrallelize simulations for reproductability.
Bug fix for reconstructing delta (use funtion append).
Handle case with only one shift (drop = FALSE)
Fix position of root during initialization.
Project the matrix of predictores, too.