A collection of helper functions for multiple
regression models fitted by lm(). Most of them are simple
functions for simple tasks which can be done with coding,
but may not be easy for occasional users of R. Most of
the tasks addressed are those sometimes needed when
using the 'manymome' package (Cheung and Cheung, 2023,
lm() 
(Version 0.4.5, updated on 2026-06-29, release history)
A collection of helper functions for
multiple regression models fitted by lm().
Most of them are simple functions for
simple tasks which can be done with coding,
but may not be easy for occasional users of R.
For more information on this package, please visit its GitHub page:
https://sfcheung.github.io/lmhelprs/
The stable CRAN version can be installed by install.packages():
install.packages("lmhelprs")
The latest developmental version of this package can be installed by remotes::install_github:
remotes::install_github("sfcheung/lmhelprs")
Most of the tasks I covered are those sometimes I needed when
using the manymome package
(Cheung & Cheung, 2023)
and the stdmod package
(Cheung, Cheung, Lau, Hui, and Vong, 2022).
Therefore, when ready, these two packages
will make use of the functions from
lmhelprs. However, most of the functions
can also be used in other scenarios.
Therefore, I named it lmhelprs.
Cheung, S. F., & Cheung, S.-H. (2023). 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. https://doi.org/10.3758/s13428-023-02224-z
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.
If you have any suggestions and found any bugs, please feel to open a GitHub issue. Thanks.