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Showing content from https://github.com/ropensci/software-review/issues/603 below:

Fit a cosinor model using the glmmTMB framework. · Issue #603 · ropensci/software-review · GitHub

Date accepted: 2024-01-09

Submitting Author Name: Rex Parsons
Submitting Author Github Handle: @RWParsons
Other Package Authors Github handles: @oliverjayasinghe, @nicolemwhite
Repository: https://github.com/RWParsons/GLMMcosinor
Version submitted: 0.1.0
Submission type: Stats
Badge grade: silver
Editor: @Paula-Moraga
Reviewers: @sachsmc, @jcavieresg

• Paste the full DESCRIPTION file inside a code block below:

Type: Package
Package: GLMMcosinor
Title: Fit a cosinor model using a generalised mixed modelling framework
Version: 0.1.0
Authors@R: c(
    person("Rex", "Parsons", , "rex.parsons94@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6053-8174")),
    person("Oliver", "Jayasinghe", role = "aut"),
    person("Nicole", "White", role = "aut",
           comment = c(ORCID = "0000-0002-9292-0773"))
  )
Description: Fit the cosinor model using the glmmTMB framework, allowing
    for a wide range of link functions and capacity of heirachical
    structures.
License: GPL (>= 3)
URL: https://github.com/RWParsons/GLMMcosinor,
    https://rwparsons.github.io/GLMMcosinor/
BugReports: https://github.com/RWParsons/GLMMcosinor/issues
Imports:
    assertthat,
    cowplot,
    ggforce,
    ggplot2,
    glmmTMB,
    lme4,
    rlang,
    scales,
    stats
Suggests:
    cosinor,
    covr,
    dplyr,
    DT,
    flextable,
    ftExtra,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    vdiffr,
    withr
VignetteBuilder: 
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd",
    "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3

• Please indicate which of our statistical package categories this package falls under. (Please check one appropriate box below):

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses

• A pre-submission inquiry has been approved in issue#601 with the issue number of the Pres-submission inquiry-->

• Who is the target audience and what are scientific applications of this package?

People analysing rhythmic/circular data - for example, circadian biologists.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

This is the first implementation of a cosinor modelling package which can handle generalised models (link functions) in R. There are other packages in python but these are limited to count-data related families. Similarly, there are very limited other packages in R that can handle a hierarchical structure or have helpful plotting methods for the model objects. This package is based on the {cosinor} R package but that is limited to linear models. A summary of existing software is given in a table in the README.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

• What grade of badge are you aiming for? (bronze, silver, gold)

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

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.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.
• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

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.

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

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