A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.
|Author||Norbert Krautenbacher, Kevin Strauss, Maximilian Mandl, Christiane Fuchs|
|Maintainer||Norbert Krautenbacher <email@example.com>|
|Package repository||View on CRAN|
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