Package: vardpoor 0.20.3

Martins Liberts

vardpoor: Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.

Authors:Juris Breidaks [aut], Martins Liberts [aut, cre], Santa Ivanova [aut], Aleksis Jursevskis [ctb], Anthony Damico [ctb], Liliana Roze [ctb], Central Statistical Bureau of Latvia [cph, fnd]

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# Install 'vardpoor' in R:
install.packages('vardpoor', repos = c('https://csblatvia.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/csblatvia/vardpoor/issues

On CRAN:

samplingstatisticsvariance

27 exports 10 stars 1.78 score 16 dependencies 238 scripts 628 downloads

Last updated 3 years agofrom:28ed111890. Checks:OK: 3 NOTE: 4. Indexed: yes.

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Doc / VignettesOKAug 20 2024
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R-4.5-linuxNOTEAug 20 2024
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R-4.3-macOKAug 20 2024

Exports:domainincPercentilelin.ratiolinarprlinarptlinarrlinginilingini2lingpglinpoormedlinqsrlinrmirlinrmpgresidual_estvar_srsvardannualvardchangesvardchangespoorvardchangstrsvardcrosvardcrospoorvardomvardom_othstrvardomhvariance_estvariance_othstrvarpoord

Dependencies:bootclicodetoolsdata.tableforeachglueiteratorslaekenlifecyclemagrittrMASSrlangstringistringrsurveyplanningvctrs

Readme and manuals

Help Manual

Help pageTopics
Extra variables for domain estimationdomain
Estimation of weighted percentilesincPercentile
Linearization of the ratio estimatorlin.ratio
Linearization of at-risk-of-poverty ratelinarpr
Linearization of at-risk-of-poverty thresholdlinarpt
Linearization of the aggregate replacement ratiolinarr
Linearization of the Gini coefficient Ilingini
Linearization of the Gini coefficient IIlingini2
Linearization of the gender pay (wage) gap.lingpg
Linearization of the median income of individuals below the At Risk of Poverty Thresholdlinpoormed
Linearization of the Quintile Share Ratiolinqsr
Linearization of the relative median income ratiolinrmir
Linearization of the relative median at-risk-of-poverty gaplinrmpg
Residual estimation of calibrationresidual_est
The estimation of the simple random sampling.var_srs
Variance estimation for measures of annual net change or annual for single and multistage stage cluster sampling designsvardannual
Variance estimation for measures of change for single and multistage stage cluster sampling designsvardchanges
Variance estimation for measures of change for sample surveys for indicators on social exclusion and povertyvardchangespoor
Variance estimation for measures of annual net change or annual for single stratified sampling designsvardchangstrs
Variance estimation for cross-sectional, longitudinal measures for single and multistage stage cluster sampling designsvardcros
Variance estimation for cross-sectional, longitudinal measures for indicators on social exclusion and povertyvardcrospoor
Variance estimation of the sample surveys in domain by the ultimate cluster methodvardom
Variance estimation for sample surveys in domain by the two stratificationvardom_othstr
Variance estimation for sample surveys in domain for one or two stage surveys by the ultimate cluster methodvardomh
Variance estimation for sample surveys by the ultimate cluster methodvariance_est
Variance estimation for sample surveys by the new stratificationvariance_othstr
Estimation of the variance and deff for sample surveys for indicators on social exclusion and povertyvarpoord