lpSolve, Hmisc, lme4 (>= 1.0), stats, graphics, grDevices,
utils
mgcv, quantreg, VGAM, SuppDists, survival, parallel

We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <doi:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.

Dustin Tingley <dtingley@gov.harvard.edu>,
Teppei Yamamoto <teppei@mit.edu>,
Kentaro Hirose <hirose@princeton.edu>,
Luke Keele <ljk20@psu.edu>,
Kosuke Imai <imai@harvard.edu>
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.

Dustin Tingley <dtingley@gov.harvard.edu>,
Teppei Yamamoto <teppei@mit.edu>,
Kentaro Hirose <hirose@princeton.edu>,
Luke Keele <ljk20@psu.edu>,
Kosuke Imai <kimai@princeton.edu>,
Minh Trinh <mdtrinh@mit.edu>,
Weihuang Wong <wwong@mit.edu>
lpSolve, Hmisc, lme4 (>= 1.0), stats, graphics, grDevices,
utils, boot
mgcv, quantreg, VGAM, SuppDists, survival, parallel,
testthat, speedglm

Simulation-Based Sensitivity Analysis for Causal Mediation
Studies
We implement the simulation-based sensitivity analysis for causal mediation studies using the methods
proposed by Qin and Yang (2020). It numerically and graphically evaluates the sensitivity of causal mediation analysis results
to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods.
First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as
sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the
strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured
pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in
different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based
methods. It is applicable to both randomized experiments and observational studies.