(by Trevor Hastie) Glmnet_1.8 uploaded to CRAN – This is a major revision, with two additional models included. 1) Multiresponse regression – family=”mgaussian” Here we have a matrix of M responses, and we fit a series of linear models in parallel. We use a group-lasso penalty on the set of M coefficients for each variable. This means they are all in or out together 2) family=”multinomial, type.multinomial=”grouped” Same story = multinomial regression, but now the group lasso penalty ensures all the coefficients are in or out for each class at the same time. We have left the default as type.multinomial=”ungrouped” because currently this grouped version is about 10 times slower. We will be looking to improve this aspect. Thanks to Noah Simon for his work on developing the algorithms for both these options. A report is in the works.