large-scale regression in R with a sparse feature matrix

Question

I\'d like to do large-scale regression (linear/logistic) in R with many (e.g. 100k) features, where each example is relatively sparse in the feature space---e.g., ~1k non-ze

Answer 1

A belated answer: glmnet will also support sparse matrices and both of the regression models requested. This can use the sparse matrices produced by the Matrix package. I advise looking into regularized models via this package. As sparse data often involves very sparse support for some variables, L1 regularization is useful for knocking these out of the model. It's often safer than getting some very spurious parameter estimates for variables with very low support.