The Ultimate Guide to the StratifiedSampling Package in R

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Explore valuable documentation and insights to make the most of the StratifiedSampling package in R. Get ready to unlock the full potential of the StratifiedSampling package!

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What is the StratifiedSampling package?

In this section, we’ll delve into the fundamental aspects and key features of the package.

The StratifiedSampling package aids in the design and analysis of stratified random sampling procedures. It provides functions for variance estimation, sample size determination, and optimal allocation of resources, supporting robust and efficient survey design.

Title: Different Methods for Stratified Sampling
Description: Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) . The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé .
Author: Raphael Jauslin [aut, cre] (), Esther Eustache [aut], Bardia Panahbehagh [aut] (), Yves Tillé [aut] ()
Maintainer: Raphael Jauslin

How to install the StratifiedSampling package?

In this section, we’ll walk you through the process of installing and loading the StratifiedSampling package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.

# Install the {Package} package if it's not already installed
if (!require("{Package}")) {
    install.packages("{Package}")
}


# Load the {Package} package
library("{Package}")

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What package information should you know?

In this section, we’ll go over the technical aspects of the StratifiedSampling package.

Key features

Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the StratifiedSampling package, visit our database of R datasets.
Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the StratifiedSampling package, you can visit our visit our database of R vignettes.
Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the StratifiedSampling package, visit our database of R package citations.

Technical details

License type: GPL (>= 2). For license details, visit the Open Source Initiative website.
Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The StratifiedSampling package has compilation requirements.
Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The StratifiedSampling package has the following required dependencies: Matrix, R (>= 3.5.0).
Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The StratifiedSampling package has the following suggested dependencies: knitr, rmarkdown, BalancedSampling, stats, testthat, StatMatch, laeken, prettydoc, ggplot2, viridis, rgeos, geojsonio, sf, rmapshaper.
External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The StratifiedSampling package uses the following external sources: RcppArmadillo, Rcpp.
Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The StratifiedSampling package has the following imported packages: Rcpp, transport, proxy, MASS, sampling, Rglpk.
Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The StratifiedSampling package has no enhancements.

How to get help with the StratifiedSampling package?

In this section, we’ll discuss a variety of available resources for getting help with the StratifiedSampling package.

Key resources

The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("StratifiedSampling") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
Package website: The StratifiedSampling package has a dedicated website. You can visit: https://github.com/RJauslin/StratifiedSampling.
Developer support: You can email Raphael Jauslin . For contact information, visit our R community directory.

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