Three key functionalities are present. It is able to mask confidential data using multiplicative noise. It is able to unmask this data while still preserving confidentiality. It is able to calculate the numerical joint density function of the original data from the unmasked data, as well as obtaining a sample from the marginal density functions of the unmasked data. The final results are a reasonable approximation to the original data for the purposes of analysis (Lin et al. (2018) <http://www.tdp.cat/issues16/abs.a271a17.php>).
| Version: | 1.0 |
| Imports: | ks, np, plyr, parallel, MASS |
| Published: | 2018-05-22 |
| Author: | Yan-Xia Lin [aut, cre], Luke Mazur [aut, cre], Jordan Morris [ctb] |
| Maintainer: | Luke Mazur <lm810 at uowmail.edu.au> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | MaskJointDensity results |
| Reference manual: | MaskJointDensity.pdf |
| Package source: | MaskJointDensity_1.0.tar.gz |
| Windows binaries: | r-devel: MaskJointDensity_1.0.zip, r-release: MaskJointDensity_1.0.zip, r-oldrel: MaskJointDensity_1.0.zip |
| macOS binaries: | r-release: MaskJointDensity_1.0.tgz, r-oldrel: MaskJointDensity_1.0.tgz |
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