Other codes

TS-LSNM:
https://github.com/gkikuchi/TSLSNM
G. Kikuchi and S. Shimizu. Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise. In Proc. Causal Analysis Workshop 2023 (CAWS2023), PMLR 223:20-39, 2023. 

Cyclic causal discovery:
https://github.com/cdt15/lingd
G. Lacerda, P. Spirtes, J. Ramsey, and P. O. Hoyer. Discovering cyclic causal models by independent components analysis. In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI'08). AUAI Press, Arlington, Virginia, USA, 366– 374, 2008.

adaptive logistic lasso (Python wrapper):
https://github.com/cdt15/ada_logistic_reg 

LiNA:
https://github.com/cdt15/lina
Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto and Z. Hao. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. In Proc. of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), 2021: 2097--2103.