(Version 0.1.1, updated on 2025-09-21, release history)
power4mome
This package is for power analysis and sample size determination for moderation, mediation, and moderated mediation.
It includes functions for power analysis and sample size determination for moderation, mediation, and moderated mediation effects in models fitted by structural equation modeling (SEM) or multiple linear regression. For SEM, both latent variable models and path models of observed variables are supported.
For more information on this package, please visit its GitHub page:
https://sfcheung.github.io/power4mome/
A get-started guide illustrates how to use this package:
https://sfcheung.github.io/power4mome/articles/power4mome.html
Code templates are also available for common mediation, moderation, and moderated mediation models:
https://sfcheung.github.io/power4mome/articles/
Philosophy
The package was developed with this philosophy:
Easy to specify the population models, even with latent variables.
As few manually-set numbers as possible when specifying the population models, with convenient default values.
As few restrictions as possible on the form of the models, even when the models have latent factors.
As automatic as possible in finding the sample size with the target power.
To achieve this comes with some costs, and some of the goals conflict with other goals: Being flexible usually means being less user-friendly, and being easy to specify the model usually means not supporting some models.
Therefore, we also try to
balance these goals, and
allow the functions to be used in different ways, to accommodate scenarios that prioritize these goals differently.
Installation
The stable version at CRAN can be installed by install.packages():
install.packages("power4mome")The latest developmental version of this package can be installed by remotes::install_github:
remotes::install_github("sfcheung/power4mome")Background
Some of us the developers have developed the package
manymome
(Cheung & Cheung, 2024)
for computing and testing effects in
models with mediation, mediation,
or moderated mediation. The tests are
usually done by simulation-based methods
such as Monte Carlo or bootstrap
confidence intervals, due to the complicated
sampling distributions of the effects.
Therefore, there are no simple ways to
determine the power of the test analytically
and accurately. The computation becomes
more complicated when latent variables
are involved, necessitating a simulation-based
method to estimate the sample size.
There are already many excellent packages out there for estimating power in structural equation modeling in general, and some are also specifically for mediation or moderated mediation. We are not intended to replace with them or reinvent the wheel. We just want to have a tool that meet our own needs:
(a) It leverages on the flexibility of manymome
in testing an indirect effect or
conditional effect with little limitations
on the model.
(b) It allows users (us and our collaborators) to specify the population model as easy (quickly) as typical power analysis programs.
We ourselves know how to do the power estimation on our own by simulation, if necessary. However, time is usually a concern, and we would like to have a tool that, though specifically designed with mediation, moderation, and moderated mediation in mind and may be limited in scope (though it is a "big" scope), is easy for our daily use in estimating power.
So here it is, power4mome, developed
with we ourselves as the users, but we
believe are also useful for others who
need to do power analysis for mediation,
moderation, and moderated mediation.
Not Just That ...
But power4mome is not just for mediation,
moderation, and moderated mediation. We
avoided wrote the functions just
for these effects, and have left room for
testing other effects, as hinted in some
examples in the help pages. They may be
introduced later. For now, supporting
effects that can be tested by manymome
is our priority.
Issues
If you have any suggestions and found any bugs, please feel free to open a GitHub issue:
https://github.com/sfcheung/power4mome/issues
Thanks.
Reference
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