Type: Package
Package: hidetify
Title: Identify Influential Observations in High Dimension
Version: 0.0.1
Author: Amadou Barry
Maintainer: Amadou Barry <barryhafia@gmail.com>
Description: Efficient tool for identifying influential observations in
    high dimensional linear regression. The tool implements two detection
    techniques single detection (Barry et al. (2020)
    <doi:10.1080/03610926.2020.1841793>) and multiple detection (Barry et
    al. (2021) <arXiv:2105.12286>).  The single detection is an adaptation
    of Cook's measure for high dimensional data. The method relies on the
    concept of expectile to construct an influence measure based on
    asymmetric correlations. The multiple detection technique applies a
    group deletion procedure to build the algorithm on three main steps.
    The first stage applies an ultra conservative score to mitigate the
    swamping effect, the second stage uses the clean sample generated in
    the previous stage and applies an aggressive score to attenuate the
    masking phenomenon. Finally, the last step is concerned with the
    validation of the influential set generated by the two previous steps.
    The main functions take a response variable and a design matrix as
    input and output a set of potential influential observations.
License: GPL-3
URL: https://doi.org/10.1080/03610926.2020.1841793,
        https://arxiv.org/abs/2105.12286
BugReports: https://github.com/AmBarry/hidetify/issues
Depends: R (>= 3.6)
Imports: MASS, Rcpp (>= 1.0.7), stats
Suggests: rmarkdown, testthat (>= 3.0.0)
LinkingTo: Rcpp, RcppArmadillo
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-08-18 23:50:56 UTC; Amadou.Barry
Repository: CRAN
Date/Publication: 2021-08-20 12:40:08 UTC
