influence.ME provides a collection of tools for
calculating measures of influential data for generalized mixed
effects models. It analyses models that were estimated using
lme4. The basic rationale behind identifying influential data
is that when iteratively single units are omitted from the
data, models based on these data should not produce
substantially different estimates. To standardize the
assessment of how influential a (single group of)
observation(s) is, several measures of influence are common
practice, such as DFBETAS and Cooks' Distance. In addition, we
provide a measure of percentage change of the fixed point
estimates and a simple test to detect changing levels of
significance.

Case Influence in Structural Equation Models
influence.SEM provides a set of tools for evaluating several measures of case influence for Structural Equation Models following the approach of Pek and MacCallum (2011).

Influence measures in Structural Equation Models: a set of tools for evaluating several measures of case influence for Structural Equation Models following the approach of Pek and MacCallum (2011).

A set of tools for evaluating several measures of case influence for structural equation models. Functions are based on the approach of J Pek and RC MacCallum (Sensitivity analysis in structural equation models: cases and their influence, Multivariate Behavioral Research, 2011).

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

Identify Influential Observations in Binary Classification
c(
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")
)
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.
dplyr, geigen, ggplot2, ggrepel, methods, ROCR
Bo-Shiang Ke [cre, aut, cph],
Yuan-chin Ivan Chang [aut],
Wen-Ting Wang [aut]