This website accompanies the paper:

- Selective inference for group-sparse linear models.

Fan Yang, Rina Foygel Barber, Prateek Jain, & John Lafferty. arXiv:1607.08211

This paper develops post-selection confidence intervals for the effect size of a group of variables in a linear regression, which can be applied after a group-sparse model selection procedure such as forward stepwise regression, iterative hard thresholding, or the group lasso.

Here we give code in the R language providing an implementation of the selective confidence intervals and p-values for each of the three model selection methods, and reproducing the simulation results and real data results in the paper.