Inference Based on Non-Probability Samples
Checks the variable balance between the probability and non-probabilit...
Returns coefficients of the underlying models
Returns confidence intervals for estimated mean
Control parameters for inference
Control parameters for outcome model
Control parameters for the selection model
Extracts estimates from the nonprob class object
Mass imputation using the generalized linear model method
Mass imputation using nearest neighbours matching method
Mass imputation using non-parametric model method
Mass imputation using predictive mean matching method
Propensity Score Model Functions
Returns the number of rows in samples
Inference with non-probability survey samples
Plots the estimated mean(s) and their confidence interval(s)
Returns population size (estimated or fixed)
Print method for the nonprob_summary object
Summary statistics for model of the nonprob class
The update method for the nonprob object with changed arguments or par...
Extracts the inverse probability weights
Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) <doi:10.1080/01621459.2019.1677241>, Yang et al. (2020) <doi:10.1111/rssb.12354>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Yang et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm> and Wu (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>. For details on the package and its functionalities see <doi:10.48550/arXiv.2504.04255>.
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