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https://github.com/sfcheung/manymome

Moderation, mediation, and moderated mediation in structural equation modeling and multiple regression
https://github.com/sfcheung/manymome

bootstrapping confidence-interval lavaan manymome mediation moderated-mediation moderation r r-package regression sem standardized-effect-size structural-equation-modeling

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Moderation, mediation, and moderated mediation in structural equation modeling and multiple regression

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README 

          

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(Version 0.3.3.1, updated on 2026-01-08, [release history](https://sfcheung.github.io/manymome/news/index.html))



# manymome  



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:



- Cheung, S. F., & Cheung, S.-H. (2024).

  *manymome*: An R package for computing

  the indirect effects, conditional

  effects, and conditional indirect

  effects, standardized or unstandardized,

  and their bootstrap confidence intervals,

  in many (though not all) models.

  *Behavior Research Methods, 56*(5),

  4862--4882.

  https://doi.org/10.3758/s13428-023-02224-z



# What Can It Do?



- Compute an unstandardized or *standardized* *indirect* *effect* or

  *conditional* *indirect* *effect* in a path model.



- Form the *confidence* *interval* for this effect.

  Nonparametric bootstrapping is fully supported,

  while Monte Carlo is supported for models

  fitted by `lavaan::sem()`.



- Multigroup models fitted by `lavaan::sem()`

  are also supported in 0.1.14.2 and later versions.

  Details can be found in

  this [article](https://sfcheung.github.io/manymome/articles/med_mg.html).



# Advantages



- **A Simpler Workflow**



  No need to define any parameters or similar

  code when

  fitting a model in `lavaan::sem()`. Just focus on fitting

  the model first. After a model has

  been selected, users can compute the effect for nearly any path, from

  nearly any variable, to nearly any other variables, conditional on

  nearly any moderators,

  and at any levels of the moderators.

  (See `vignette("manymome")` for details.) This is particularly

  convenient for multigroup models fitted by `lavaan::sem()`,

  which are supported in 0.1.14.2 and later versions

  (see [this guide](https://sfcheung.github.io/manymome/articles/med_mg.html),

  for an illustration).



- **Supports Both SEM-Based and Regression-Based Analysis**



  Supports structural equation models fitted by `lavaan::sem()` or by

  path models fitted by regression using `lm()`, although the

  focus of this package is on structural equation models.

  The interface of the main functions are nearly the same for

  both approaches.



- **Flexible in the Form of Models**



  No limit on the number of predictors, mediators, and

  outcome variables, other than those by `lavaan::sem()` and `lm()`.

  For multigroup models fitted by `lavaan::sem()`,

  there is no inherent limit on the number of groups,

  other than the limit due to `lavaan::sem()`, if any

  (supported in 0.1.14.2 and later versions).



- **Supports Standardized Effects**



  Can estimate standardized indirect effects and standardized

  conditional indirect effects without the need to standardize

  the variables. The bootstrap and Monte Carlo

  confidence intervals for standardized

  effects correctly take into account the sampling variation

  of the standardizers (the standard deviations of the predictor

  and the outcome variable) by recomputing them in each bootstrap

  sample or replication.



- **Supports Missing Data**



  Supports datasets with missing data

  through `lavaan::sem()` with full information maximum likelihood (`fiml`).



  In version 0.2.7.3 or later, it also supports missing data handled

  by multiple imputation if the models are fitted by `lavaan.mi::sem.mi()`

  or `lavaan.mi::lavaan.mi()` (see `vignette("do_mc_lavaan_mi")`).

  (Support for `semTools::runMI()` and its wrappers was removed in

  version 0.2.7.3 because this and related functions were deprecated

  in `semTools`.)



- **Supports Numeric and Categorical Moderators**



  Supports numeric and

  categorical moderators. It has a function (`factor2var()`) for the easy

  creation of dummy variables in `lavaan::sem()`, and can also capitalize on

  the native support of categorical moderators in `lm()`.



- **Less Time for Bootstrapping**



  Bootstrapping, which can be time consuming, can

  be conducted just once. The main functions for computing indirect effects

  and conditional indirect effects can be called as many times as needed without redoing

  bootstrapping because they can reuse pregenerated bootstrap

  estimates (see `vignette("manymome")` and `vignette("do_boot")`).



- **Supports Latent Variables Mediation**



  Supports indirect effects among

  latent variables for models fitted by `lavaan::sem()` (see

  `vignette("med_lav")`).



- **Support Treating Group As a Moderator**



  For multigroup models fitted by `lavaan::sem()`, it supports

  comparing the direct or indirect effects along any path

  between any two groups. That is, it uses the grouping variable

  as a moderator (illustrated [here](https://sfcheung.github.io/manymome/articles/med_mg.html);

  supported in 0.1.14.2 and later versions).



# Limitations



Despite the aforementioned advantages, the current version of

`manymome` has the following limitations:



- Does not (officially) support

  categorical `x`-variables with more

  than two categories. Note that most

  functions will work with dichotomous

  `x`-variables, and the effect of `x`

  is simply the difference in predicted

  `y` between the two levels if coded as

  0 and 1 (or any coding with a

  difference of 1, e.g., 1 and 2).



- Does not support multilevel models (although `lavaan` does).



- For bootstrapping, only supports nonparametric bootstrapping,

and supports only percentile and bias-corrected

confidence interval. Does not support other bootstrapping methods such parametric bootstrapping.



- Only supports OLS estimation when `lm()` is used.



We would add more to this list (suggestions are welcomed by

adding [GitHub issues](#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](https://sfcheung.github.io/manymome/articles/index.html) (vignettes) on

special topics, such as how to use `mod_levels()` to set

the levels of the moderators. More will be added.



# Homepage



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



https://sfcheung.github.io/manymome/



# Installation



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



```r

install.packages("manymome")

```



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



```r

remotes::install_github("sfcheung/manymome")

```



# Background



We developed the package [`stdmod`](https://sfcheung.github.io/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.



# Issues



If you have any suggestions and found any bugs or limitations, please feel

feel to open a GitHub issue. Thanks.



https://github.com/sfcheung/manymome/issues