Package: BrainCon
Type: Package
Title: Inference the Partial Correlations Based on Time Series Data
Version: 0.3.0
Author: Yunhaonan Yang [aut, cre],
  Peng Wu [aut],
  Xin Gai [aut],
  Yumou Qiu [aut],
  Xiaohua Zhou [aut]
Authors@R: c(
  person("Yunhaonan", "Yang", email = "haonan_yy@pku.edu.cn", role = c("aut", "cre")),
  person("Peng", "Wu", email = "wupeng@bicmr.pku.edu.cn", role = "aut"),
  person("Xin", "Gai", email = "gaitianmu@outlook.com", role = "aut"),
  person("Yumou", "Qiu", email = "yumouqiu@iastate.edu", role = "aut"),
  person("Xiaohua", "Zhou", email = "azhou@math.pku.edu.cn", role = "aut"))
Maintainer: Yunhaonan Yang <haonan_yy@pku.edu.cn>
Description: A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using  the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: glmnet, MASS
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2023-05-22 06:37:46 UTC; Dinic
Repository: CRAN
Date/Publication: 2023-05-22 06:50:02 UTC
Built: R 4.6.0; ; 2026-04-26 06:12:48 UTC; unix
