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(Version 0.3.4, updated on 2026-03-26, [release history](https://sfcheung.github.io/manymome/news/index.html))

# manymome  <img src="man/figures/logo.png" align="right" height="150" />

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
