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NonProbEst  

Estimation in Nonprobability Sampling
View on CRAN: Click here


Download and install NonProbEst package within the R console
Install from CRAN:
install.packages("NonProbEst")

Install from Github:
library("remotes")
install_github("cran/NonProbEst")

Install by package version:
library("remotes")
install_version("NonProbEst", "0.2.4")



Attach the package and use:
library("NonProbEst")
Maintained by
Luis Castro Martín
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-18
Latest Update: 2020-06-03
Description:
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
How to cite:
Luis Castro Martín (2019). NonProbEst: Estimation in Nonprobability Sampling. R package version 0.2.4, https://cran.r-project.org/web/packages/NonProbEst. Accessed 05 Mar. 2026.
Previous versions and publish date:
0.1.0 (2019-06-18 18:10), 0.2.0 (2019-09-07 10:00), 0.2.1 (2019-09-30 14:50), 0.2.2 (2019-11-29 12:40), 0.2.3 (2019-12-17 18:00)
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