Description
Date accepted: 2022-09-20
Submitting Author Name: Eunseop Kim
Submitting Author Github Handle: @markean
Repository: https://github.com/markean/melt
Version submitted: 1.6.0.9000
Submission type: Stats
Badge grade: silver
Editor: @Paula-Moraga
Reviewers: @pchausse, @awstringer1
Due date for @awstringer1: 2022-08-29
Archive: TBD
Version accepted: TBD
Language: en
- Paste the full DESCRIPTION file inside a code block below:
Type: Package
Package: melt
Title: Multiple Empirical Likelihood Tests
Version: 1.6.0.9000
Authors@R: c(
person("Eunseop", "Kim", , "kim.7302@osu.edu", role = c("aut", "cre")),
person("Steven", "MacEachern", role = c("ctb", "ths")),
person("Mario", "Peruggia", role = c("ctb", "ths"))
)
Description: Performs multiple empirical likelihood tests for linear and
generalized linear models. The core computational routines are
implemented using the 'Eigen' C++ library and 'RcppEigen' interface,
with OpenMP for parallel computation. Details of multiple testing
procedures are given in Kim, MacEachern, and Peruggia (2021)
<arxiv:2112.09206>.
License: GPL (>= 2)
URL: https://github.com/markean/melt, https://markean.github.io/melt/
BugReports: https://github.com/markean/melt/issues
Depends:
R (>= 4.0.0)
Imports:
graphics,
methods,
Rcpp,
stats
Suggests:
covr,
ggplot2,
knitr,
microbenchmark,
rmarkdown,
spelling,
testthat (>= 3.0.0),
withr
LinkingTo:
BH,
dqrng,
Rcpp,
RcppEigen
VignetteBuilder:
knitr
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
NeedsCompilation: yes
Roxygen: list (markdown = TRUE, roclets = c ("namespace", "rd",
"srr::srr_stats_roclet"))
RoxygenNote: 7.2.0
Pre-submission Inquiry
- A pre-submission inquiry has been approved in issue #549
General Information
-
Who is the target audience and what are scientific applications of this package?
-
Paste your responses to our General Standard G1.1 here, describing whether your software is:
The package attempts the first implementation of the nested bilevel optimization approach within R to compute constrained empirical likelihood. The inner layer Newton-Raphson method for empirical likelihood is written in C++, enabling faster computation than other routines written in R. -
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
Yes.
Badging
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Silver. -
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- Compliance with a good number of standards beyond those identified as minimally necessary.
- Have a demonstrated generality of usage beyond one single envisioned use case.
In my opinion, the generality comes from the applicability of empirical likelihood methods to (generalized) linear models and the ability to test linear hypotheses of choice.
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Confirm each of the following by checking the box.
- I have read the rOpenSci packaging guide.
- I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
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This package:
- does not violate the Terms of Service of any service it interacts with.
- has a CRAN and OSI accepted license.
- contains a README with instructions for installing the development version.
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The package is on CRAN. - Do you intend for this package to go on Bioconductor?
Code of conduct
- I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.