Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/ropensci/software-review/issues/595 below:

Species range dynamics simulation toolset · Issue #595 · ropensci/software-review · GitHub

Date accepted: 2024-01-21
Submitting Author Name: Katarzyna Markowska
Submitting Author Github Handle: @katarzynam-165
Other Package Authors Github handles: @LechoslawKuczynski
Repository: https://github.com/popecol/rangr
Version submitted: 1.0.0
Submission type: Stats
Badge grade: silver
Editor: @adamhsparks
Reviewers: taddallas, @taddallas, @TheAnalyticalEdge

Due date for taddallas: 2023-08-16

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

Package: rangr
Type: Package
Title: Mechanistic Simulation of Species Range Dynamics
Version: 1.0.0
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: Species range dynamics simulation toolset.
License: MIT + file LICENSE
Imports:
    methods,
    parallel,
    pbapply,
    grDevices,
    graphics,
    stats,
    utils,
    zoo,
    terra,
    raster,
    assertthat
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    covr,
    bookdown
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Roxygen: list (markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
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

• 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#<592>

• 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.

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

  • The first implementation of a novel algorithm; or
  • The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
  • An improvement on other implementations of similar algorithms in R.

  Please include hyperlinked references to all other relevant software.

  rangr is the first implementation of a novel algorithm, but there are a few packages like RangeShiftR, poems, or steps that 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

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

  Silver

• 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)

  Have a demonstrated generality of usage beyond one single envisioned use case. Software is frequently developed for one particular use case envisioned by the authors themselves. Generalising the utility of software so that it is readily applicable to other use cases, and satisfactorily documenting such generality of usage, represents another aspect which may be considered sufficient for software to attain a silver grade.

  This aspect is particularly well-suited due to the versatile range of applications offered by rangr. These applications include:

  • modelling population range dynamics,

  • testing various ecological scenarios,

  • testing species distribution modelling methods.

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.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4