A Collection of Techniques Correcting for Sample Selection Bias

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) . The methods may be used for further purposes where weighting and generation of new observations is needed.


Reference manual

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0.1.0 by Norbert Krautenbacher, 3 years ago

Browse source code at https://github.com/cran/sambia

Authors: Norbert Krautenbacher , Kevin Strauss , Maximilian Mandl , Christiane Fuchs

Documentation:   PDF Manual  

GPL-3 license

Imports stats, mvtnorm, dplyr, smotefamily, e1071, ranger, pROC, FNN

See at CRAN