Imports: devtools, dplyr, extrafont, ggplot2, grid, gridGraphics,
gridSVG, hunspell, knitr, magrittr, moments, nortest,
reticulate, rmarkdown, roloc, rolocISCCNBS, utils, whisker,

Description: Blind useRs do not have access to the graphical output
from R without printing the content of graph windows to an
embosser of some kind. This is not as immediate as is required
for efficient access to statistical output. The functions here
are created so that blind people can make even better use of R.

SystemRequirements: Python 3 and wxPython 4.0

person("Sophie", "Banks", email = "sb@xxx.com", role = "ctb"),
gridSVG, hunspell, knitr, magrittr, mathjaxr, moments, nortest,
reticulate, rlang, rmarkdown, roloc, rolocISCCNBS, utils,
Suggests: ggfortify, installr, markdown, spelling

Authors@R: person(c("A.", "Jonathan", "R."), "Godfrey", role=c("aut", "cre"), email="a.j.godfrey@massey.ac.nz")

Author: A. Jonathan R. Godfrey [aut, cre]
Authors@R: person("A. Jonathan R.", "Godfrey", role=c("aut", "cre"), email="a.j.godfrey@massey.ac.nz")

Title: Inference the Partial Correlations Based on Time Series Data
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"))
Description: A statistical tool to inference the multi-level partial correlations based on multi-subject time series data. 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 can be used to identify nonzero individual- or population-level partial correlations, and find out unequal partial correlation coefficients between two populations.

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.