sambia
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) doi:10.1155/2017/7847531. The methods may be used for further purposes where weighting and generation of new observations is needed.
- Version0.1.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release06/06/2018
Team
Norbert Krautenbacher
Kevin Strauss
Maximilian Mandl
Christiane Fuchs
Insights
Last 30 days
This package has been downloaded 118 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.
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Last 365 days
This package has been downloaded 1,657 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 71 downloads.
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Dependencies
- Imports7 packages