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cvcrand
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]
[Scholar Profile | Author Map]
All associated links for this package
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 21 Nov. 2024.
Previous versions and publish date:
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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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