SLOPE
An efficient and fully-featured package for Sorted L-One Penalized Estimation
SLOPE (Sorted L-One Penalized Estimation) is a regularization method for high-dimensional regression that controls the false discovery rate while performing variable selection. Unlike LASSO which uses a single penalty parameter, SLOPE uses a sequence of sorted penalties that provide better statistical properties for multiple testing scenarios.
This package provides efficient, production-ready implementations across R, Python, and Julia, with a unified C++ core for performance. It includes support for generalized linear models, sparse matrices, and modern optimization algorithms, making it suitable for large-scale statistical learning problems in genomics, econometrics, and machine learning.
Citation
@misc{larsson2025,
author = {Larsson, Johan and Bogdan, Malgorzata and Grzesiak, Krystyna
and Massias, Mathurin and Wallin, Jonas},
publisher = {arXiv},
title = {Efficient Solvers for {SLOPE} in {R,} {Python,} {Julia,} and
{C++}},
number = {arXiv:2511.02430},
date = {2025/11/04},
url = {http://arxiv.org/abs/2511.02430},
doi = {10.48550/arXiv.2511.02430},
langid = {en}
}