GH_Quadrature

<p>This is purely for user's curiousity, modifying this will not affect the workings of the package as it is either hardcoded in or taken as a parameter.</p>

datasets

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>).

Luke Mazur

MaskJointDensity

Masking, Unmasking and Restoring Confidential Data

GH_Quadrature function

<p>The format is:
 num [1:7] 0.000548 0.030757 0.240123 0.457143 0.240123 ...</p>

GH_Quadrature: The seven weights used for Gaussian Hermite Quadrature

Description

Usage

Arguments

Format

Examples