https://github.com/statistikat/surveysd

Estimate standard deviation of estimates in complex surveys using bootstrap weights.
https://github.com/statistikat/surveysd

bootstrap error-estimation r survey

Last synced: about 1 year ago
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Estimate standard deviation of estimates in complex surveys using bootstrap weights.

• Host: GitHub
• URL: https://github.com/statistikat/surveysd
• Owner: statistikat
• License: gpl-2.0
• Created: 2017-09-08T10:00:51.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2025-04-10T09:17:14.000Z (about 1 year ago)
• Last Synced: 2025-04-10T10:45:38.905Z (about 1 year ago)
• Topics: bootstrap, error-estimation, r, survey
• Language: R
• Homepage: https://statistikat.github.io/surveysd
• Size: 11.6 MB
• Stars: 9
• Watchers: 5
• Forks: 4
• Open Issues: 3
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE

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README

          
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output: github_document

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# surveysd 

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This is the development place for the R-package `surveysd`. The package can be used to estimate 

the standard deviation of estimates in complex surveys using bootstrap weights.

## Installation

```{r, eval = FALSE}

# Install release version from CRAN

install.packages("surveysd")

# Install development version from GitHub

devtools::install_github("statistikat/surveysd")

```

## Concept

Bootstrapping has long been around and used widely to estimate confidence intervals and standard 

errors of point estimates. This package aims to combine all necessary steps for applying a calibrated

bootstrapping procedure with custom estimating functions.

## Workflow

A typical workflow with this package consists of three steps. To see these concepts in practice,

please refer to the [getting started vignette].

* Calibrated weights can be generated with the function `ipf()` using an iterative proportional updating algorithm.

* Bootstrap samples are drawn with rescaled bootstrapping in the function `draw.bootstrap()`.

* These samples can then be calibrated with an iterative proportional updating algorithm using 

`recalib()`.

* Finally, estimation functions can be applied over all bootstrap replicates with `calc.stError()`.

## Further reading

More information can be found on the [github-pages site] for surveysd.

* The methodology is covered in the [methodology vignette].

* A more comprehensive documentation of `calc.stError()` is available in the [error estimation vignette].

  

[methodology vignette]:      https://statistikat.github.io/surveysd/articles/methodology.html

[error estimation vignette]: https://statistikat.github.io/surveysd/articles/error_estimation.html

[getting started vignette]:  https://statistikat.github.io/surveysd/articles/surveysd.html

[github-pages site]:         https://statistikat.github.io/surveysd