The R Journal: article published in 2020, volume 12:1

The R package NonProbEst for estimation in non-probability surveys PDF download
M. Rueda, R. Ferri-García and L. Castro , The R Journal (2020) 12:1, pages 406-418.

Abstract Different inference procedures are proposed in the literature to correct selection bias that might be introduced with non-random sampling mechanisms. The R package NonProbEst enables the estimation of parameters using some of these techniques to correct selection bias in non-probability surveys. The mean and the total of the target variable are estimated using Propensity Score Adjustment, calibration, statistical matching, model-based, model-assisted and model-calibratated techniques. Confidence intervals can also obtained for each method. Machine learning algorithms can be used for estimating the propensities or for predicting the unknown values of the target variable for the non-sampled units. Variance of a given estimator is performed by two different Leave-One-Out jackknife procedures. The functionality of the package is illustrated with example data sets.

Received: 2020-04-05; online 2020-09-10
CRAN packages: NonProbEst, caret, sampling, survey
CRAN Task Views implied by cited CRAN packages: OfficialStatistics, HighPerformanceComputing, MachineLearning, Multivariate, SocialSciences, Survival

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {M. Rueda and R. Ferri-García and L. Castro},
  title = {{The R package NonProbEst for estimation in non-probability
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-015},
  url = {},
  pages = {406--418},
  volume = {12},
  number = {1}