NLMR

[marcosci]

Summary

• What does this package do? (explain in 50 words or less):

  • Provides neutral landscape models and utility functions to work with and extend them. NLMR uses raster as its geospatial framework and can therefore easily be used in landscape analysis to test against the influence of landscape patterns and serve as a basis for spatial simulation modeling.

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

Package: NLMR
Type: Package
Title: Simulating Neutral Landscape Models
Version: 0.2
Authors@R: c(person("Marco", "Sciaini", email = "sciaini.marco@gmail.com", role = c("aut", "cre")), 
    person("Matthias", "Fritsch", email = "matthias.fritsch@forst.uni-goettingen.de", role =  c("aut")),
    person("Craig", "Simpkins", email = "simpkinscraig063@gmail.com", role =  c("aut")),
    person("Cédric", "Scherer", email = "cedricphilippscherer@gmail.com", role =  c("ctb"), comment = "Implemented nlm_neigh"))
Description: Provides neutral landscape models 
    (Gardner et al. 1987 <doi:10.1007/BF02275262>,
    With 1997 <doi:10.1046/j.1523-1739.1997.96210.x>) that can easily extend in 
    existing landscape analyses. Neutral landscape models range from "hard" 
    neutral models (completely random distibuted), to "soft" neutral models (definable spatial characteristics) and 
    generate landscape patterns that are independent of ecological processes.
    Thus, these patterns can be used as null models in landscape ecology. 'NLMR' 
    combines a large number of algorithms from other published software for simulating neutral landscapes (Saura & 
    Martínez 2000   <doi:10.1023/A:1008107902848>, Etherington et
    al. 2015 <doi:10.1111/2041-210X.12308>) and
    includes utility functions to classify and combine the multiple landscapes. The 
    simulation results are obtained in a geospatial data format (raster* objects 
    from the 'raster' package) and can, therefore, be used 
    in any sort of raster data operation that is performed with standard 
    observation data.                                                                                                  
License: GPL-3
Copyright: file inst/COPYRIGHTS
Encoding: UTF-8
LazyData: true
ByteCompile: true
Depends:
    R (>= 3.1.0)
RoxygenNote: 6.0.1
Imports:
    checkmate,
    dismo,
    dplyr,
    ggplot2,
    igraph,
    magrittr,
    purrr,
    RandomFields,
    raster,
    rasterVis,
    sp,
    spatstat,
    stats,
    tibble,
    viridis,
    extrafont
URL: https://marcosci.github.io/NLMR/
BugReports: https://github.com/marcosci/NLMR/issues/
Suggests:
    testthat,
    covr,
    knitr,
    rmarkdown,
    lintr
VignetteBuilder:
    knitr

• URL for the package (the development repository, not a stylized html page):

  • https://github.com/marcosci/NLMR

• Please indicate which category or categories from our package fit policies this package falls under *and why(? (e.g., data retrieval, reproducibility. If you are unsure, we suggest you make a pre-submission inquiry.):

  • geospatial data, because the package simulates land use patterns in a geospatial format.
  • Exploratory data analysis, because the package can be used as a very fundamental null hypothesis in any analysis that formulate questions around the influence of landscape pattern on processes (e.g. ecological, social processes),

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

  • Target audience: Mainly ecologists, since neutral landscape models are a well-established method in their field. But the package itself can be used by a broad audience, as it is convenient way to quickly prototype spatial simulation models.
  • Scientific applications: Formulating null hypotheses in a landscape analysis, test and develop landscape metrics, gradient analysis of the influence of landscape patterns on ecological processes, starting point for spatial simulations, such as agent-based models.

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

  • There was ecomodtools, which could simulate a small fraction of the neutral landscape models in NLMR. But it is not under active development anymore and does not compile under newer R versions.

•   If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.

Requirements

Confirm each of the following by checking the box. 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.
• includes documentation with examples for all functions.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, including reporting of test coverage, using services such as Travis CI, Coveralls and/or CodeCov.
• I agree to abide by ROpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

Publication options

• Do you intend for this package to go on CRAN? (it already is)
• Do you wish to automatically submit to the Journal of Open Source Software? If so:
  • The package has an obvious research application according to JOSS's definition.
  • The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
  • The package is deposited in a long-term repository with the DOI:
  • (Do not submit your package separately to JOSS)
• Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:
  • The package is novel and will be of interest to the broad readership of the journal.
  • The manuscript describing the package is no longer than 3000 words.
  • You intend to archive the code for the package in a long-term repository which meets the requirements of the journal.
  • (Please do not submit your package separately to Methods in Ecology and Evolution)

Detail

• Does R CMD check (or devtools::check()) succeed? Paste and describe any errors or warnings:

  • No errors/warnings

• Does the package conform to rOpenSci packaging guidelines? Please describe any exceptions:

• If this is a resubmission following rejection, please explain the change in circumstances:

• If possible, please provide recommendations of reviewers - those with experience with similar packages and/or likely users of your package - and their GitHub user names:

[marcosci]

Hi Folks,

was quite unsure about the fit of our package into the rOpenSci world - but we would love to get your review and platform.

Best wishes,
Marco

[sckott]

thanks for your submission @marcosci we've been discussing and will get back to you asap

[maelle]

Editor checks:

• [x ] Fit: The package meets criteria for fit and overlap
• [ x] Automated tests: Package has a testing suite and is tested via Travis-CI or another CI service.
• [ x] License: The package has a CRAN or OSI accepted license
• [ x] Repository: The repository link resolves correctly
• Archive (JOSS only, may be post-review): The repository DOI resolves correctly
• Version (JOSS only, may be post-review): Does the release version given match the GitHub release (v1.0.0)?

---

Editor comments

Thanks for your submission @marcosci! I'm currently looking for reviewers. I have these comments/questions:

• goodpractice::gp() returned "♥ Ahh! Rad package! Keep up the tiptop work!" which I had to pass on. 😉

• the copyright file is for a font, where is this font used?

• please add this badge to the repo README

---

Reviewers: @laurajanegraham @jhollist
Due date: 2018-02-21

[marcosci]

Hey @maelle and @sckott,

thanks a lot for the effort :)

The font is used in the theme function (theme_nlm) and can be imported via
util_import_roboto_condensed.

Which badge? :) There seems to be something missing in your reply.

[maelle]

Oops indeed! 🙀 [![](https://badges.ropensci.org/188_status.svg)](https://github.com/ropensci/onboarding/issues/188)

[maelle]

@laurajanegraham @jhollist thanks for accepting to review this package! 😺

Your review is due on 2018-02-21.

Please find here our reviewing guide and the review template.