Package: NonProbEst 0.2.4

NonProbEst: Estimation in Nonprobability Sampling

Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.

Authors:Luis Castro Martín <[email protected]>, Ramón Ferri García <[email protected]> and María del Mar Rueda <[email protected]>

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# Install 'NonProbEst' in R:
install.packages('NonProbEst', repos = c('https://luiscastro193.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 scripts 216 downloads 17 exports 79 dependencies

Last updated 4 years agofrom:978c6f6d64. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winNOTENov 21 2024
R-4.5-linuxNOTENov 21 2024
R-4.4-winNOTENov 21 2024
R-4.4-macNOTENov 21 2024
R-4.3-winNOTENov 21 2024
R-4.3-macNOTENov 21 2024

Exports:calib_weightsconfidence_intervalfast_jackknife_variancegeneric_jackknife_variancejackknife_variancelee_weightsmatchingmean_estimationmodel_assistedmodel_basedmodel_calibratedprop_estimationpropensitiessc_weightstotal_estimationvalliant_weightsvd_weights

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlpSolvelubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartsamplingscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr