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: 2026-06-18 07:19:03 UTC; root
Imports: R6
RoxygenNote: 7.0.1
Repository: https://siyavashshabani.r-universe.dev
Date/Publication: 2020-05-06 11:08:40 UTC
RemoteUrl: https://github.com/siyavashshabani/ghost
RemoteRef: HEAD
RemoteSha: 71aa751ef40e9d98bca25f74644e5aec45c7d589