Lifecycle: stable Project Status: Active - The project has reached a stable, usable state and is being actively developed. CRAN status CRAN: Release Date CRAN RStudio mirror downloads Code size Last Commit at Main R-CMD-check

(Version 0.2.2, updated on 2024-06-05, release history)


Functions for estimating indirect effects, conditional indirect effects, and conditional effects in a model with moderation, mediation, and/or moderated mediation fitted by structural equation modelling (SEM) or estimated by multiple regression. The package was introduced in:

What Can It Do?



Despite the aforementioned advantages, the current version of manymome has the following limitations:

We would add more to this list (suggestions are welcomed by adding GitHub issues) so that users (and we) know when other tools should be used instead of manymome, or whether we can address these limitations in manymome in the future.

How To Use It?

A good starting point is the Get-Started article (vignette("manymome")).

There are also articles (vignettes) on special topics, such as how to use mod_levels() to set the levels of the moderators. More will be added.


For more information on this package, please visit its GitHub page:


The stable version at CRAN can be installed by install.packages():


The latest developmental-but-stable version at GitHub can be installed by remotes::install_github():



We developed the package stdmod in 2021 for moderated regression. We included a function (stdmod::stdmod_lavaan()) for standardized moderation effect in path models fitted by lavaan::sem(). However, in practice, path models nearly always included indirect effects and so moderated mediation is common in path models. Moreover, stdmod is intended for moderated regression, not for structural equation modeling. We thought perhaps we could develop a more general tool for models fitted by structural equation modelling based on the interface we used in stdmod::stdmod_lavaan(). In our own projects, we also need to estimate indirect effects in models frequently. Large sample sizes with missing data are also common to us, for which bootstrapping is slow even with parallel processing. Therefore, we developed manymome to address these needs.


If you have any suggestions and found any bugs or limitations, please feel feel to open a GitHub issue. Thanks.