A new suite of software packages has been released for R, Python, Julia, and C++ that efficiently addresses the Sorted L-One Penalized Estimation (SLOPE) problem. These packages implement a hybrid coordinate descent algorithm, which enhances computational speed and memory efficiency. They are designed to fit generalized linear models and support a variety of loss functions, broadening their applicability in statistical modeling. The development aims to provide fast and resource-conscious tools for researchers and practitioners working with SLOPE. This release reflects ongoing efforts to improve optimization methods in statistical machine learning. By supporting multiple programming languages, the packages facilitate integration into diverse analytical workflows. Overall, these tools represent a significant advancement in solving penalized estimation problems efficiently.