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surveysd

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

# 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.

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Version

Install

install.packages('surveysd')

Monthly Downloads

619

Version

2.0.1

License

GPL (>= 2)

Issues

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Maintainer

Johannes Gussenbauer

Last Published

March 17th, 2026

Functions in surveysd (2.0.1)

kishFactor

Kish Factor
print.summary.ipf

Print method for IPF calibration summary
plot.surveysd

Plot surveysd-Objects
print.surveysd

Print function for surveysd objects
rescaled.bootstrap

Draw bootstrap replicates
recalib

Calibrate weights
summary.ipf

Generate Summary Output for IPF Calibration
PointEstimates

Weighted Point Estimates
generate.HHID

Generate new houshold ID for survey data with rotating panel design taking into account split households
demo.eusilc

Generate multiple years of EU-SILC data
get.selection

Get sample selection (~deltas) from drawn bootstrap replicates
cpp_mean

Calculate mean by factors
ipf

Iterative Proportional Fitting
calc.stError

Calcualte point estimates and their standard errors using bootstrap weights.
computeLinear

Numerical weighting functions
ipf_step

Perform one step of iterative proportional updating
draw.bootstrap

Draw bootstrap replicates