samplingVarEst: Sampling Variance Estimation

Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets.

Version: 1.5
Depends: R (≥ 3.1.0)
Published: 2023-01-14
Author: Emilio Lopez Escobar [aut, cre, cph], Ernesto Barrios Zamudio [ctb], Juan Francisco Munoz Rosas [ctb]
Maintainer: Emilio Lopez Escobar <emilio at quantos.mx>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://www.quantos.mx/, https://www.itam.mx/
NeedsCompilation: yes
Classification/ACM: G.3
Classification/JEL: C13, C15, C42, C83
Classification/MSC: 62D05, 62F40, 62G09, 62H12
Citation: samplingVarEst citation info
Materials: README ChangeLog
In views: OfficialStatistics
CRAN checks: samplingVarEst results

Documentation:

Reference manual: samplingVarEst.pdf

Downloads:

Package source: samplingVarEst_1.5.tar.gz
Windows binaries: r-devel: samplingVarEst_1.5.zip, r-release: samplingVarEst_1.5.zip, r-oldrel: samplingVarEst_1.5.zip
macOS binaries: r-release (arm64): samplingVarEst_1.5.tgz, r-oldrel (arm64): samplingVarEst_1.5.tgz, r-release (x86_64): samplingVarEst_1.5.tgz
Old sources: samplingVarEst archive

Reverse dependencies:

Reverse imports: RRTCS

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