Package: Ghost
Type: Package
Title: Missing Data Segments Imputation in Multivariate Streams
Version: 0.1.0
Author: Siyavash Shabani, Reza Rawassizadeh 
Maintainer: Siyavash Shabani <s.shabani.aut@gmail.com>
Description: Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.
URL:
        https://www.researchgate.net/publication/332779980_Ghost_Imputation_Accurately_Reconstructing_Missing_Data_of_the_Off_Period
Depends: R (>= 2.10)
License: GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2020-03-23 22:25:06 UTC; lorman
Imports: R6
RoxygenNote: 7.0.1
Repository: CRAN
Date/Publication: 2020-03-25 16:50:05 UTC
Built: R 4.6.0; ; 2026-04-26 19:02:57 UTC; unix
