Amelia is an R package for the multiple imputation of incomplete data. Multiple imputation is a method to overcome the computational problem of missing data while maintaining good statistical properties. Amelia uses a bootstrap-based algorithm that increases speed and robustness. In addition, Amelia includes a graphical user interface that requires no knowledge of R at all.
causalsens is an R package to implement the selection bias approach to sensitivity analysis for causal effects as introduced in Blackwell (2014). This approach allows researchers to evaulate the effect of unmeasured confounders on their estimated effects varying both the strength and direction of the confounding.
CEM is a package for R, Stata, and SPSS that implements the method of coarsened exact matching. CEM improves causal inferences and reduces model dependence by making observations more comparable. I helped to write the Stata and SPSS packages.
DirectEffects is an R package to estimate controlled direct effects. As of now, the only model supported is sequential g-estimation, but we plan to expand to other models, including doubly robust estimators, in the future. For more information on how CDEs can be useful for applied research and a brief introduction to sequential g-estimation, see our 2016 APSR. Note that this package is still in alpha stages and is under rapid development.