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.
Version: 0.1.0 Imports: stats, mvtnorm, dplyr, smotefamily, e1071, ranger, pROC, FNN Published: 2018-06-06 DOI: 10.32614/CRAN.package.sambia Author: Norbert Krautenbacher, Kevin Strauss, Maximilian Mandl, Christiane Fuchs Maintainer: Norbert Krautenbacher <norbert.krautenbacher at tum.de> License: GPL-3 NeedsCompilation: no CRAN checks: sambia results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sambia to link to this page.
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