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chest  

Change-in-Estimate Approach to Assess Confounding Effects
View on CRAN: Click here


Download and install chest package within the R console
Install from CRAN:
install.packages("chest")

Install from Github:
library("remotes")
install_github("cran/chest")

Install by package version:
library("remotes")
install_version("chest", "0.3.7")



Attach the package and use:
library("chest")
Maintained by
Zhiqiang Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-16
Latest Update: 2023-03-23
Description:
Applies the change-in-effect estimate method to assess confounding effects in medical and epidemiological research (Greenland & Pearce (2016) ). It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183
How to cite:
Zhiqiang Wang (2019). chest: Change-in-Estimate Approach to Assess Confounding Effects. R package version 0.3.7, https://cran.r-project.org/web/packages/chest. Accessed 06 Mar. 2026.
Previous versions and publish date:
0.2.0 (2019-12-16 16:00), 0.3.1 (2020-01-29 13:20), 0.3.2 (2020-07-09 07:20), 0.3.3 (2020-07-12 09:20), 0.3.4 (2020-09-30 09:10), 0.3.5 (2021-01-19 08:10), 0.3.6 (2022-03-01 16:20)
Other packages that cited chest R package
View chest citation profile
Other R packages that chest depends, imports, suggests or enhances
Complete documentation for chest
Functions, R codes and Examples using the chest R package
Some associated functions: chest . chest_clogit . chest_cox . chest_forest . chest_glm . chest_lm . chest_nb . chest_plot . diab_df . 
Some associated R codes: chest.R . chest_clogit.R . chest_cox.R . chest_forest.R . chest_glm.R . chest_lm.R . chest_nb.R . chest_plot.R . data_diab_df.R .  Full chest package functions and examples
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