SWSamp is a general purpose package to provide a suite of functions for the sample size calculations and power analysis in a Stepped Wedge Trial. Contains functions for closed-form sample size calculation (based on a set of specific models) and simulation-based procedures that can extend the basic framework.
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README.md

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SWSamp is a general purpose package to provide a suite of functions for the sample size calculations and power analysis in a Stepped Wedge Trial. Contains functions for closed-form sample size calculation (based on a set of specific models) and simulation-based procedures that can extend the basic framework.

Installation

There are two ways of installing SWSamp. A "stable" version is packaged and binary files are available for Windows and as source. To install the stable version on a Windows machine, run the following commands

install.packages("SWSamp",
    repos=c("http://www.statistica.it/gianluca/R",
        "https://cran.rstudio.org",
        "https://www.math.ntnu.no/inla/R/stable"),
    dependencies=TRUE
)

Note that you need to specify a vector of repositories - the first one hosts SWSamp, while the second one should be an official CRAN mirror. You can select whichever one you like, but a CRAN mirror must be provided, so that install.packages() can also install the "dependencies" (e.g. other packages that are required for survHE to work). The third one is used to install the package INLA, which can be used to perform simulation-based sample size calculations using a Bayesian approach. This process can be quite lengthy, if you miss many of the relevant packages.

To install from source (e.g. on a Linux machine), run

install.packages("SWSamp",
    repos=c("http://www.statistica.it/gianluca/R",
        "https://cran.rstudio.org",
        "https://www.math.ntnu.no/inla/R/stable"),
    type="source",
    dependencies=TRUE

The second way involves using the "development" version of SWSamp - this will usually be updated more frequently and may be continuously tested. On Windows machines, you need to install a few dependencies, including Rtools first, e.g. by running

pkgs <- c("foreach", "doParallel", "iterators", "parallel", "Matrix","lme4","INLA","Rtools","devtools")
repos <- c("https://cran.rstudio.com", "https://www.math.ntnu.no/inla/R/stable") 
install.packages(pkgs,repos=repos,dependencies = "Depends")

before installing the package using devtools:

devtools::install_github("giabaio/SWSamp")

Under Linux or MacOS, it is sufficient to install the package via devtools:

install.packages("devtools")
devtools:install_github("giabaio/SWSamp")