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cvcrand  

Efficient Design and Analysis of Cluster Randomized Trials
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("cvcrand", "0.1.1")



Attach the package and use:
library("cvcrand")
Maintained by
Hengshi Yu
[Scholar Profile | Author Map]
First Published: 2017-11-28
Latest Update: 2023-09-17
Description:
Constrained randomization by Raab and Butcher (2001) is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) . Motivated from Li, et al. (2016) , the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.
How to cite:
Hengshi Yu (2017). cvcrand: Efficient Design and Analysis of Cluster Randomized Trials. R package version 0.1.1, https://cran.r-project.org/web/packages/cvcrand. Accessed 10 May. 2025.
Previous versions and publish date:
0.0.1 (2017-11-28 20:06), 0.0.2 (2018-04-17 00:14), 0.0.3 (2019-02-27 15:20), 0.0.4 (2019-03-25 19:53), 0.1.0 (2020-04-13 21:00)
Other packages that cited cvcrand R package
View cvcrand citation profile
Other R packages that cvcrand depends, imports, suggests or enhances
Complete documentation for cvcrand
Functions, R codes and Examples using the cvcrand R package
Some associated functions: Dickinson_design . Dickinson_outcome . cptest . cvcrand . cvrall . cvrcov . 
Some associated R codes: Dickinson_design.R . Dickinson_outcome.R . cptest.R . cvcrand_package.R . cvrall.R . cvrcov.R .  Full cvcrand package functions and examples
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