[thaowang] L0cpt 0.2.0

tianhaowang@mail.ustc.edu.cn (Tianhao Wang) — Mon, 23 Mar 2026 17:20:28 GMT

Under an L0 penalty framework, a computationally efficient implementation of change point detection is developed. By integrating active set algorithms with warm start initialization, the package achieves linear-time complexity for solving change point detection problems. References: Wen et al. (2020) ; Zhu et al. (2020).

[thaowang] L0TFinv 0.1.0

tianhaowang@mail.ustc.edu.cn (Tianhao Wang) — Tue, 10 Jun 2025 09:38:12 GMT

Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) ; Zhu et al. (2020); Wen et al. (2023) .

thaowang.r-universe.dev

[thaowang] L0cpt 0.2.0

[thaowang] L0TFinv 0.1.0