We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998615583902>.

We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.

Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model
design attributes in the fit.  rms is a collection of about 180
functions that assist and streamline modeling, especially for
biostatistical and epidemiologic applications.  It also
contains new functions for binary and ordinal logistic
regression models and the Buckley-James multiple regression
model for right-censored responses, and implements penalized
maximum likelihood estimation for logistic and ordinary linear
models.  rms works with almost any regression model, but it was
especially written to work with binary or ordinal logistic
regression, Cox regression, accelerated failure time models,
ordinary linear models, the Buckley-James model, generalized
least squares for serially or spatially correlated
observations, generalized linear models, and quantile
regression.