I am looking for a package or other solution in R that can evaluate indirect effects and meets all of the following criteria:
* Can create bootstrapped CIs around an indirect effect (or can implement any other method of creating asymmetric CIs) * Can address nested data (e.g., through multilevel/mixed effects) * Can allow for fully continuous X variables * Can address missing data (e.g., using multiple imputation via a package such as mice; I have a non-normally distributed mediator so cannot use ML for all estimation) Any input on what would address these criteria would be greatly appreciated. Here are the packages I have tried so far: * lavaan.survey - can do all of the above except for bootstrap estimation of the indirect effect (lavaan is great but cannot do multilevel, lavaan.survey is also great but cannot do the bootstrap estimate) * mediation - Has many strong features, but limits the X (treatment) variable to take 2 values at a time, whereas I have dozens of X values (from an observational study) * piecewiseSEM - Is very flexible and allows for multilevel data structure and multiple distributions, but does not have bootstrap/asymmetric CIs for indirect effects ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
