c(person("Diogo", "Ferrari", email="diogoferrari@gmail.com", role=c("aut", "cre")),
person("John", "Jackson", email="jjacksn@umich.edu", role=c("aut")))
Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.
Diogo Ferrari [aut, cre],
John Jackson [aut]

c(person("James", "Balamuta", email = "balamut2@illinois.edu", role = c("aut", "cre","cph")),
person("Peter", "Kovesi", email = "peter.kovesi@uwa.edu.au", role = c("cph"), comment="Creator of CET Color Maps"))
Collection of perceptually uniform colour maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700>
at the Centre for Exploration Targeting (CET).
James Balamuta [aut, cre, cph],
Peter Kovesi [cph] (Creator of CET Color Maps)

c(person("James", "Balamuta", email = "balamut2@illinois.edu", role = c("aut", "cre","cph"),
comment = c(ORCID = "0000-0003-2826-8458")),
person("Peter", "Kovesi", email = "peter.kovesi@uwa.edu.au", role = c("cph"),
comment = "Creator of CET Color Maps"))
Collection of perceptually uniform colour maps made by Peter Kovesi
(2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700>
at the Centre for Exploration Targeting (CET).
James Balamuta [aut, cre, cph]
(<https://orcid.org/0000-0003-2826-8458>),
Peter Kovesi [cph] (Creator of CET Color Maps)

Ceteris Paribus Plots (What-If Plots) for a Single Observation
Ceteris Paribus Plots (What-If Plots) are designed to present model
responses around a single point in a feature space.
For example around a single prediction for an interesting observation.
Plots are designed to work in a model-agnostic fashion, they are working
for any predictive Machine Learning model and allow for model comparisons.
The Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.

Ceteris Paribus Profiles (What-If Plots) are designed to present model
responses around selected points in a feature space.
For example around a single prediction for an interesting observation.
Plots are designed to work in a model-agnostic fashion, they are working
for any predictive Machine Learning model and allow for model comparisons.
Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
randomForest, ggiraph, e1071, testthat, rpart