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Fast implementations of survey sampling algorithms for single-stage probability sampling from finite populations. Provides equal probability methods (simple random sampling, systematic, Bernoulli), unequal probability methods (conditional Poisson / maximum entropy, Brewer, systematic PPS, Pareto piPS, sequential Poisson, Poisson, Chromy's minimum replacement, multinomial), and balanced sampling via the cube method. All sampling functions return design objects carrying sample indices, inclusion probabilities, and design metadata. Generics compute joint inclusion probabilities, pairwise expectations, and sampling covariances for variance estimation. Mostly based on algorithms from Tille (2006, doi:10.1007/0-387-34240-0 ).

Unequal Probability Sampling

  • unequal_prob_wor() - Without replacement: CPS (maximum entropy), Brewer, systematic PPS, Poisson, SPS (sequential Poisson), Pareto

  • unequal_prob_wr() - With replacement: Chromy (minimum replacement), multinomial PPS

Equal Probability Sampling

Balanced Sampling

  • balanced_wor() - Cube method (Deville & Tille, 2004) for balanced sampling with unequal probabilities, with optional stratification (Chauvet & Tille, 2006)

Design Queries

All sampling functions return objects of class "sondage_sample". Use these generics to query the design:

Utilities

References

Tille, Y. (2006). Sampling Algorithms. Springer Series in Statistics.

Chromy, J.R. (2009). Some generalizations of the Horvitz-Thompson estimator. Memorial JSM.

Author

Maintainer: Ahmadou Dicko mail@ahmadoudicko.com (ORCID)

Other contributors: