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

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

Inference the Partial Correlations Based on Time Series Data
Yunhaonan Yang [aut, cre],
Peng Wu [aut],
Xin Gai [aut],
Yumou Qiu [aut],
Xiaohua Zhou [aut]
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"))
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.

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.

Glm Fitting and Variable Selection using 'RcppArmadillo'
person("Jacob", "Seedorff", , "jwseedorff@uiowa.edu", role = c("aut", "cre"))
Fits glms using 'RcppArmadillo', can use Fisher's scoring, BFGS, or L-BFGS.
Can also make use of parallel computation to speed up fitting process via 'OpenMP'.
Also can perform best subset selection with an efficient branch and bound algorithm
that allows best subset selection for more variables.