**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.

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