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Abstract: The sparse group-lasso is an advanced statistical technique that involves the lasso and group-lasso penalties, which enables the sparsity at both the individual and group level regarding the natural grouping structure when solving high-dimensional learning problems. However, estimating the regularization paths can be computationally hard if the designed matrix input is large. To improve the computational efficiency, we develop an R package ’sparsegl' according to an algorithm by iteratively applying the KKT Stationarity Condition and the Strong Rule, which helps to identify the active and inactive predictors before updating their coefficients. Furthermore, we empirically illustrate its efficiency by comparing the running time to other existing packages for solving lasso-type problems.