Presubmission Inquiry: rangr

[katarzynam-165]

Submitting Author Name: Katarzyna Markowska Submitting Author Github Handle: !--author1-->@katarzynam-165<!--end-author1-- Other Package Authors Github handles: !--author-others-->@LechoslawKuczynski<!--end-author-others-- Repository: https://github.com/popecol/rangr Submission type: Pre-submission Language: en

---

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

Package: rangr
Type: Package
Title: Mechanistic Simulation of Species Range Dynamics
Version: 0.1.0
Authors@R: c(
    person("Katarzyna", "Markowska", email = "katarzyna.markowska@amu.edu.pl", role = c("aut", "cre")),
    person("Lechosław", "Kuczyński", email = "lechu@amu.edu.pl", role = "aut"))
Description: Set of tools for simulating species range dynamics.
License: MIT + file LICENSE
Imports:
    methods,
    data.table,
    parallel,
    pbapply,
    grDevices,
    graphics,
    stats,
    utils,
    zoo,
    terra,
    raster
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    covr,
    bookdown
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)
Depends: 
    R (>= 3.5.0)
Config/testthat/edition: 3
URL: https://github.com/popecol/rangr,
    https://popecol.github.io/rangr/
BugReports: https://github.com/popecol/rangr/issues

Scope

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):

  Data Lifecycle Packages

  • [ ] data retrieval

  • [ ] data extraction

  • [ ] data munging

  • [ ] data deposition

  • [ ] data validation and testing

  • [ ] workflow automation

  • [ ] version control

  • [ ] citation management and bibliometrics

  • [ ] scientific software wrappers

  • [ ] field and lab reproducibility tools

  • [ ] database software bindings

  • [ ] geospatial data

  • [ ] text analysis

    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

  • [x] Spatial Analyses

  • [x] Time Series Analyses

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

I chose these categories because rangr package allows simulations to be performed in both the spatial and temporal dimensions. In my opinion, none of the categories seem to fit perfectly, so I am not sure which would be more suitable.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

The documentation of standards are not incorporated yet, but this will be done as soon as doubts about the category issue are resolved.

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

rangr is an R package designed for simulating species range dynamics, primarily aimed at ecologists and conservationists who work with complex data structures such as those derived from citizen science and wildlife monitoring programs. With rangr, users can mimic the key processes that shape population numbers and spatial distributions, including local dynamics, dispersal, and habitat selection, to project population responses to environmental changes. Additionally, rangr can be used to test and evaluate different methods of modelling species distribution using simulated data as a reference.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

The same - no, similar - yes. Packages like RangeShiftR, poems, or MegaSDM serve similar purposes. However, none of them met all the criteria that were important to us in this type of simulation, such as being easy to set up and customize with other existing R functions, supporting simulations that vary in both time and space, and incorporating the Virtual Ecologist approach by providing functions for various sampling scenarios.

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

Not applicable.

• Any other questions or issues we should be aware of?:

For some reason the pkgcheck function gives me this message:

Package has no continuous integration checks.

I've set CI up using GitHub Actions in this file, and everything seems to be working correctly, so I don't really understand where this message is coming from, and would appreciate your help.

[maelle]

@katarzynam-165 thanks a lot for your presubmission inquiry!

Our definition of time series software is software which applies algorithms to 'classical' time-series data, generally in time-series specific classes. The temporal aspects of your (rangr) package are almost entirely coded as incremental development steps, with the internal routines for each step being then spatial and not temporal. As such, we would see this as a spatial package, and would welcome a full submission.

Regarding the pkgcheck error,

• have you set up a GitHub token locally? https://usethis.r-lib.org/articles/git-credentials.html might help
• instead of running pkgcheck locally, you could set up a pkgcheck workflow for GitHub Actions, see https://docs.ropensci.org/pkgcheck/reference/use_github_action_pkgcheck.html