RIS ID
104418
Abstract
Lin (2014) developed a framework of the method of the sample-moment-based density approximant, for estimating the probability density function of microdata based on noise multiplied data. Theoretically, it provides a promising method for data users in generating the synthetic data of the original data without accessing the original data; however, technical issues can cause problems implementing the method. In this paper, we describe a software package called MaskDensity14, written in the R language, that uses a computational approach to solve the technical issues and makes the method of the sample-moment-based density approximant feasible. MaskDensity14 has applications in many areas, such as sharing clinical trial data and survey data without releasing the original data.
Publication Details
Lin, Y. & Fielding, M. James. (2015). MaskDensity14: an R package for the density approximant of a univariate based on noise multiplied data. SoftwareX, 3-4 37-43.