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  "Title": "ADMM for High-Dimensional Mediation Models",
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  "Description": "We use the Alternating Direction Method of Multipliers\n(ADMM) for parameter estimation in high-dimensional,\nsingle-modality mediation models. To improve the sensitivity\nand specificity of estimated mediation effects, we offer the\nsure independence screening (SIS) function for dimension\nreduction. The available penalty options include Lasso, Elastic\nNet, Pathway Lasso, and Network-constrained Penalty. The\nmethods employed in the package are based on Boyd, S., Parikh,\nN., Chu, E., Peleato, B., & Eckstein, J. (2011).\n<doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008)\n<doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008)\n<doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996)\n<doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X.\n(2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T.\n(2005) <doi:10.1111/j.1467-9868.2005.00503.x>.",
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