Roadmap 0.1.0-0.3.0

[BERENZ]

The initial version of the package should have:

Version 0.1.0

1. Mass imputation -- families: gaussian, binomial, Gamma, inverse.gaussian, poisson, quasi* and MASS::negative.binomial.

• Estimator: using model based approach (stats::glm and MASS::glm.nb)
• Estimator: using predictive mean matching (RANN::nn2)
• Variance estimator: Kim et al. (2021), p. 950.

Literature:

• Kim, J. K., Park, S., Chen, Y., & Wu, C. (2021). Combining non-probability and probability survey samples through mass imputation. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184(3), 941–963. https://doi.org/10.1111/rssa.12696

Version 0.2.0

2. Propensity score - binomial() with logit, probit and cloglog links

• unit-level survey data is available
• only population totals are available

Literature:

• Chen, Y., Li, P., & Wu, C. (2020). Doubly Robust Inference With Nonprobability Survey Samples. Journal of the American Statistical Association, 115(532), 2011–2021. https://doi.org/10.1080/01621459.2019.1677241

Version 0.3.0

3. Doubly robust

• standard : Chen, Li and Wu (2020)
• with minimization of asymptotic bias: Yang, Kim and Rui (2020)

Literature:

• Chen, Y., Li, P., & Wu, C. (2020). Doubly Robust Inference With Nonprobability Survey Samples. Journal of the American Statistical Association, 115(532), 2011–2021. https://doi.org/10.1080/01621459.2019.1677241
• Kim, J. K., & Wang, Z. (2018). Sampling Techniques for Big Data Analysis. International Statistical Review, 1, 1–15. https://doi.org/10.1111/insr.12290
• Yang, S., Kim, J. K., & Rui, S. (2020). Doubly robust inference when combining probability and non-probability samples with high dimensional. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 82(2), 445–465. https://doi.org/10.1111/rssb.12354

[BERENZ]

Closed because of #6