Imports: utils, rscala (>= 3.2.18), commonsMath (>= 1.2.5)

Authors@R: c(person(given="David B.",family="Dahl",role=c("aut","cre"),email="dahl@stat.byu.edu",comment=c(ORCID="0000-0002-8173-1547")),person(given="Richard L.",family="Warr",role=c("aut"),email="warr@stat.byu.edu",comment=c(ORCID="0000-0001-8508-3105")),person(given="Jeremy M.",family="Meyer",role=c("aut"),email="jeremymeyer23@hotmail.com"),person(given="Arthur",family="Lui",role=c("aut"),email="luiarthur@gmail.com",comment=c(ORCID="0000-0002-3652-0599")))
Imports: utils, rscala (>= 3.2.19), commonsMath (>= 1.2.5)
Author: David B. Dahl [aut, cre] (<https://orcid.org/0000-0002-8173-1547>),

Title: Tools for reading AIDA (http://aida.freehep.org/) files into R
Description: Read objects from the AIDA file and make them available

Description: Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.

Title: Help Detect and Mitigate Bias in Machine Learning Models
person("Gabriela", "de Queiroz", email = "gdq@ibm.com", role = "aut"),
person("Stacey", "Ronaghan", email = "ronaghan@us.ibm.com", role = "aut"),
person("Saishruthi", "Swaminathan", email = "saishruthi.tn@ibm.com",
Description: The 'AI Fairness 360' <https://aif360.mybluemix.net/> toolkit is an open-source library to help detect
and mitigate bias in machine learning models. The AI Fairness 360 R package includes
a comprehensive set of metrics for datasets and models to test for biases,
explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Packaged: 2020-06-15 19:15:02 UTC; saishruthi.tn@ibm.com

Title: Create Pie Like Plot for Completeness
Authors@R: person("Yusman", "Kamaleri", email = "ybkamaleri@gmail.com", role = c("aut", "cre"))
Description: Create a pie like plot to visualise if the aim or several aims of a
project is achieved or close to be achieved i.e the aim is achieved when the point is at the
center of the pie plot. Imagine it's like a dartboard and the center means 100%
completeness/achievement. Achievement can also be understood as 100%
coverage. The standard distribution of completeness allocated in the pie plot