https://github.com/sfcheung/stdmod

Functions related to standardized moderation effect
https://github.com/sfcheung/stdmod

bootstrapping confidence-interval effect-sizes moderation r r-package regression standardization standardized-moderation

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Functions related to standardized moderation effect

• Host: GitHub
• URL: https://github.com/sfcheung/stdmod
• Owner: sfcheung
• License: gpl-3.0
• Created: 2020-05-11T10:12:35.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-09-22T12:18:17.000Z (almost 2 years ago)
• Last Synced: 2025-03-16T00:47:42.919Z (over 1 year ago)
• Topics: bootstrapping, confidence-interval, effect-sizes, moderation, r, r-package, regression, standardization, standardized-moderation
• Language: R
• Homepage: https://sfcheung.github.io/stdmod/
• Size: 26.1 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE

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# stdmod: Standardized Moderation 

(Version 0.2.12, updated on 2026-01-07, [release history](https://sfcheung.github.io/stdmod/news/index.html))

(Important changes since 0.2.0.0: Bootstrap confidence intervals and

variance-covariance matrix of estimates are the defaults of `confint()`

and `vcov()` for the output of `std_selected_boot()`.)

This package includes functions for computing a standardized

moderation effect and forming its confidence interval by

nonparametric bootstrapping correctly. It was described briefly

in the following publication ([OSF project page](https://osf.io/ac8de/)).

It supports moderated regression conducted by `stats::lm()` and path

analysis with product term conducted by `lavaan::lavaan()`.

- Cheung, S. F., Cheung, S.-H., Lau, E. Y. Y., Hui, C. H., & Vong, W. N. (2022) Improving an old way to measure moderation effect in standardized units. *Health Psychology, 41*(7), 502-505. https://doi.org/10.1037/hea0001188.

More information on this package:

https://sfcheung.github.io/stdmod/

## Quick Links:

- [stdmod](https://sfcheung.github.io/stdmod/articles/stdmod.html): A quick start on how to use

  `std_selected()` and `std_selected_boot()`, the

  two main functions, to standardize selected variables

  in a regression model and refit the model.

- [moderation](https://sfcheung.github.io/stdmod/articles/moderation.html): How

  to use `std_selected()` and `std_selected_boot()` to compute standardized

  moderation effect and form its nonparametric bootstrap confidence interval.

- [std_selected](https://sfcheung.github.io/stdmod/articles/std_selected.html): How to use

  `std_selected()` to mean center or standardize selected

  variables in any regression models, and use

  `std_selected_boot()` to form nonparametric

  bootstrap confidence intervals

  for standardized regression coefficients (*betas* in

  psychology literature).

- [plotmod](https://sfcheung.github.io/stdmod/articles/plotmod.html): How to generate a typical plot of

  moderation effect using `plotmod()`.

- [cond_effect](https://sfcheung.github.io/stdmod/articles/cond_effect.html): How to compute conditional

  effects of the predictor for selected

  levels of the moderator, and form nonparametric bootstrap

  confidence intervals these effects.

# Installation

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

```

install.packages("stdmod")

```

The latest version of this package at GitHub can be

installed by `remotes::install_github()`:

```

remotes::install_github("sfcheung/stdmod")

```

# Implementation

The main function, `std_selected()`, accepts an `lm()`

output, standardizes variables by users, and update the

results. If interaction terms are present, they will be

formed after the standardization. If bootstrap

confidence intervals are requested using

`std_selected_boot()`, both standardization

and regression will be repeated in each bootstrap sample,

ensuring that the sampling variability of the standardizers

(e.g., the standard deviations of the selected variables),

are also taken into account.

# Issues

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

feel to open a GitHub issue. Thanks.