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English

The precision estimation is done by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953) with linearization for nonlinear statistics and residual estimation from the regression model to take weight calibration into account.

Latvian

Precizitāte ir novērtēta ar galīgo klāsteru metodi (Hansen, Hurwitz and Madow, 1953), ietverot linearizāciju nelineārai statistikai, kā arī regresijas modeļa atlikumu novērtēšanu gadījumos, ja ir veikta svaru kalibrācija.

Installation

Stable version from CRAN

install.packages("vardpoor")

Development version from github

remotes::install_github("CSBLatvia/vardpoor/vardpoor")

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Version

Install

install.packages('vardpoor')

Monthly Downloads

1,542

Version

0.21.0

License

EUPL

Issues

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Repository

https://github.com/CSBLatvia/vardpoor/

Homepage

https://csblatvia.github.io/vardpoor/

Maintainer

Jelena Voronova

Last Published

January 29th, 2026

Functions in vardpoor (0.21.0)

Variance estimation for measures of change for single and multistage stage cluster sampling designs

The estimation of the simple random sampling

Variance estimation for cross-sectional, longitudinal measures for single and multistage stage cluster sampling designs

Variance estimation for measures of annual net change or annual for single stratified sampling designs

Residual estimation of calibration

Linearization of the relative median at-risk-of-poverty gap

Linearization of the relative median income ratio

Linearization of the Quintile Share Ratio

vardchangespoor

Variance estimation for measures of change for sample surveys for indicators on social exclusion and poverty

Variance estimation for measures of annual net change or annual for single and multistage stage cluster sampling designs

Variance estimation for sample surveys in domain for one or two stage surveys by the ultimate cluster method

Variance estimation for cross-sectional, longitudinal measures for indicators on social exclusion and poverty

Variance estimation for sample surveys in domain by the two stratification

Variance estimation for sample surveys by the ultimate cluster method

Variance estimation of the sample surveys in domain by the ultimate cluster method

variance_othstr

Variance estimation for sample surveys by the new stratification

Estimation of the variance and deff for sample surveys for indicators on social exclusion and poverty

Linearization of the Gini coefficient I

Linearization of the Gini coefficient II

Linearization of at-risk-of-poverty threshold

Linearization of the aggregate replacement ratio

Linearization of the ratio estimator

Extra variables for domain estimation

Linearization of at-risk-of-poverty rate

Linearization of the gender pay (wage) gap

Estimation of weighted percentiles

Linearization of the median income of individuals below the At Risk of Poverty Threshold