R packages

• mr.genius: Robust Mendelian randomization with many invalid instruments [Github]

Reference: Ting Ye, Zhonghua Liu, Baoluo Sun, Eric J. Tchetgen Tchetgen. GENIUS-MAWII: For Robust Mendelian Randomization with Many Weak Invalid Instruments.

• idid: Leverage exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasrued confounding [GitHub]

Reference: Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, Dylan S. Small. Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend. [Slides]

• broad.narrow: Sensitivity analysis combining broad and narrow case definitions in matched case-control studies [GitHub].

Reference: Ting Ye and Dylan S. Small. Combining Broad and Narrow Case Definitions in Matched Case-Control Studies.

• RobinCar: Robust inference for average treatment effect under covariate-adaptive randomization [GitHub].

Reference: Ting Ye, Yanyao Yi, Jun Shao (2020). Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods.

Ting Ye, Jun Shao, Yanyao Yi, Qingyuan Zhao. Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials.

• DIDBracket: Inference for treatment effect based on a negative correlation strategy for bracketing in difference-in-differences [GitHub].

Reference: Ting Ye, Luke Keele, Raiden Hasegawa, Dylan S. Small (2020). A Negative Correlation Strategy for Bracketing in Difference-in-Differences.

• mr.divw: Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization [GitHub].

Reference: Ting Ye, Jun Shao, Hyunseung Kang (2019). Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization.