• Re Section 5.10 Screening rules for the lasso. This recent paper (Liu, Zhao, Wang and Le, 2014) proposes the "savsi" rule which is clearly superior to the DPP rule discussed in the book (Wang and Ye were coauthors of that paper), and appears to have similar performance to the strong rules. Unlike the strong rules, savsi has the advantage of being safe, i.e it is guaranteed never to fail. It is quiet complex but has a closed form.
• The glmnet package efficiently fits a number of lasso regression models. Available in R, Python or Matlab. More details of this software and other R packages implementing methods described in this book can be found here
• CVXR package for solving convex optimization problems in R is finally here (Novemeber 2017). Thanks to Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd, et al.