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.

中文翻译：

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sparsegl：用于估计稀疏组套索的 R 包

稀疏组套索是一种高维回归技术，适用于预测变量具有自然分组结构并且在组和个体预测变量级别都鼓励稀疏性的问题。在本文中，我们讨论了用于计算此类正则化模型的新 R 包。目的是提供高度优化的解决方案例程，能够分析非常大的数据集，尤其是在稀疏设计矩阵的上下文中。