Description: A set of tools for evaluating several measures of case influence for structural equation models.

Title: Identify Influential Observations in Binary Classification
person("Bo-Shiang", "Ke", email = "naivete0907@gmail.com", role = c("cre", "aut", "cph")),
person("Yuan-chin Ivan", "Chang", email = "ycchang@sinica.edu.tw", role = "aut"),
person("Wen-Ting", "Wang", email = "egpivo@gmail.com", role = "aut")
Description: Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.
Imports: dplyr, geigen, ggplot2, ggrepel, methods, ROCR

Title: Software Tools to Quantify Structural Importance of Nodes in a
person("Jacobs", "Simon", email = "sdjacobs@uchicago.edu", role = "aut"),
person("Khanna", "Aditya", email = "khanna7@uchicago.edu", role = c("aut", "cre")),
person("Madduri", "Kamesh", email = "madduri@cse.psu.edu", role = "ctb"),
person("Bader", "David", email = "bader@cc.gatech.edu", role = "ctb"))
Description: Provides functionality to compute various node centrality measures on networks.
Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
SNAP library), implementations of Burt's constraint and effective
network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's
bridging metric. On Unix systems, the betweenness, Key Players, and
bridging implementations are parallelized with OpenMP, which may run
faster on systems which have OpenMP configured.
Imports: igraph (>= 1.0.1), Matrix (>= 1.1-4), methods, utils

Title: Identification of the Most Influential Nodes (Hubs)
Description: Contains functions for reconstruction of networks from
adjacency matrices and data frames, analysis of the topology of the network
and calculation of centrality measures, and identification of the most
influential nodes (network hubs). Also, some functions have been provided
for the assessment of dependence and correlation of two network centrality
measures as well as the conditional probability of deviation from their
corresponding means in opposite direction.
Fred Viole and David Nawrocki (2013, ISBN:1490523995).
(>= 0.4.7.1), parallel, knitr, rmarkdown