The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context of sparse design matrices.
翻译:稀有的组距是一个高维回归技术,对于预测器有自然组合结构的问题和在组和个人预测器一级鼓励宽度的问题都有用。在本文件中,我们讨论了用于计算这种正规化模型的新R包。目的是提供高度优化的解决方案例行程序,以便能够分析非常庞大的数据集,特别是在设计矩阵稀少的情况下。