Graph-Constrained Regularization for Sparse Generalized Linear Models (glmgraph)
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.

Classification and Regression Training (caret)
Misc functions for training and plotting classification and regression models.

Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models (nnlasso)
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.