c(
person("Seung W.", "Choi",
email = "schoi@austin.utexas.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-4777-5420")),
person("Sangdon", "Lim",
email = "sangdonlim@utexas.edu",
role = c("aut"),
comment = c(ORCID = "0000-0002-2988-014X")),
person("Benjamin D.", "Schalet",
email = "b-schalet@northwewstern.edu",
role = c("ctb")),
person("Aaron J.", "Kaat",
email = "aaron.kaat@northwestern.edu",
role = c("ctb")),
person("David", "Cella",
email = "d-cella@northwestern.edu",
role = c("ctb")))
equate, lavaan, mirt, plink, psych, methods, mvnfast
shiny, shinythemes, shinyWidgets, shinyjs, DT, knitr,
kableExtra, testthat (>= 2.1.0), rmarkdown, dplyr, pkgdown
Seung W. Choi [aut, cre] (<https://orcid.org/0000-0003-4777-5420>),
Sangdon Lim [aut] (<https://orcid.org/0000-0002-2988-014X>),
Benjamin D. Schalet [ctb],
Aaron J. Kaat [ctb],
David Cella [ctb]

http://prosettastone.org (project description),
https://choi-phd.github.io/PROsetta/ (documentation)
'import.R' 'configPROsetta.R' 'post_functions.R'
'core_functions.R' 'datasets.R' 'example.R'
'helper_functions.R' 'linking_functions.R' 'plot_functions.R'
'runshiny.R'

https://www.prosettastone.org/ (project description),
https://choi-phd.github.io/PROsetta/ (documentation)

Bayesian Prior and Posterior Predictive Replication Assessment
Utilize the Bayesian prior and posterior predictive checking
approach to provide a statistical assessment of replication success
and failure. The package is based on the methods proposed in
Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.

Precision-Recall and ROC Curves for Weighted and Unweighted Data
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.

Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.