Software 📦

CRAN: GitHub:

New Versions

  • A new version (v2.4.5) of RNeXML is on CRAN - Semantically Rich I/O for the NeXML Format. See the release notes for changes. Checkout the docs to get started. RNeXML RNeXML
  • A new version (v1.1.0) of bold is on CRAN - interface to Bold Systems (http://www.boldsystems.org/) API. See the release notes for changes. Checkout the docs to get started. bold bold
  • A new version (v0.1.3) of outsider.base is on CRAN - base package for outsider. See the release notes for changes. Checkout the docs to get started. outsider.base outsider.base
  • A new version (v2.1.1) of GSODR is on CRAN - Global Surface Summary of the Day (GSOD) Weather Data from R. See the release notes for changes. Checkout the docs to get started. GSODR GSODR
  • A new version (v1.0.0) of rnoaa is on CRAN - NOAA Weather Data from R. See the release notes for changes. Checkout the docs to get started. rnoaa rnoaa
  • A new version (v0.6.0) of brranching is on CRAN - Fetch Phylogenies from Many Sources. See the release notes for changes. Checkout the docs to get started. brranching brranching
  • A new version (v0.4.0) of originr is on CRAN - Fetch species origin data from the web. See the release notes for changes. Checkout the docs to get started. originr originr
  • A new version (v0.2.3.0) of opentripplanner is on CRAN - client for OpenTripPlanner for journey planning. See the release notes for changes. Checkout the docs to get started. opentripplanner opentripplanner
  • A new version (v0.9.97) of taxize is on CRAN - taxonomic toolbelt for R. See the release notes for changes. Checkout the taxize book to get started. taxize taxize
  • A new version (v1.1.2) of dbparser is on CRAN - DrugBank database XML parser. See the release notes for changes. Checkout the docs to get started. dbparser dbparser
  • A new version (v1.0.0) of rbison is on CRAN - interface to the USGS BISON API. See the release notes for changes. Checkout the docs to get started. rbison rbison



Software Review ✔


Software review is now accepting submissions again.

We accept community contributed packages via our software review system - an open software review system, sorta like scholarly paper review, but way better. We’ll highlight newly onboarded packages here. A huge thanks to our reviewers, who do a lot of work reviewing (see the blog post on our review system), and the authors of the packages!

If you want to be a reviewer fill out this short form, and we’ll ping you when there’s a submission that fits in your area of expertise.

The following package was recently submitted:



On the blog

It’s time for another installment of … 2 Months in 2 Minutes - rOpenSci News, June 2020 - if you want a brief summary of the last 2 months of these newsletters, these blog posts are for you (written by Stefanie Butland)



Use Cases

The following 27 works use/cite rOpenSci software:



From the Forum

We have a discussion forum (using Discourse) for the rOpenSci community. It’s a really nice way to have conversations on the internet. From time to time we’ll highlight recent discussions of interest.


Many new use cases were shared in the forum over the past month since our last newsletter:



Call For Maintainers

Part of the mission of rOpenSci is making sustainable software that users can rely on. Some software maintainers need to give up maintenance due to a variety of circumstances. When that happens we try to find new maintainers. Check out our guidance for taking over maintenance of a package.

We’ve had nine recent examples of maintainer transitions within rOpenSci:

We’ve got one package in need of a new maintainer:

  • rflybase: The current maintainer is looking for a new maintainer. Email Scott if you’re interested.



Get involved with rOpenSci

We maintain a Contributing Guide that can help direct you to the right place, whether you want to make code contributions, non-code contributions, or other things like sharing use cases.






Keep up with rOpenSci


Footnotes

  1. Wesener, F., Szymczak, A., Rillig, M. C., & Tietjen, B. (2020). Stress priming affects fungal competition – evidence from a combined experimental and modeling study. https://doi.org/10.1101/2020.03.04.976357 

  2. Deribe, K., Simpson, H., Pullan, R. L., Bosco, M. J., Wanji, S., Weaver, N. D., … Cano, J. (2020). Predicting the Environmental Suitability and Population at Risk of Podoconiosis in Africa. https://doi.org/10.1101/2020.03.04.977827 

  3. Parravicini, V., Casey, J. M., Schiettekatte, N. M. D., Brandl, S. J., Pozas-Schacre, C., Carlot, J., … Vii, J. (2020). Global gut content data synthesis and phylogeny delineate reef fish trophic guilds. https://doi.org/10.1101/2020.03.04.977116 

  4. Vantas, K., Sidiropoulos, E., & Loukas, A. (2020). Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests. Water, 12(3), 687. https://doi.org/10.3390/w12030687 

  5. Ashby, M. P. J. (2020, March 6). Three quarters of new criminological knowledge is hidden from policy makers. https://doi.org/10.31235/osf.io/wnq7h 

  6. Yu, G. (2020). Using ggtree to Visualize Data on Tree‐Like Structures. Current Protocols in Bioinformatics, 69(1). https://doi.org/10.1002/cpbi.96 

  7. Lequime, S., Bastide, P., Dellicour, S., Lemey, P., & Baele, G. (2020). nosoi: a stochastic agent-based transmission chain simulation framework in R. https://doi.org/10.1101/2020.03.03.973107 

  8. Wei, N., Kaczorowski, R. L., Arceo-Gómez, G., O’Neill, E. M., Hayes, R. A., & Ashman, T.-L. (2020). Pollinator niche partitioning and asymmetric facilitation contribute to the maintenance of diversity. https://doi.org/10.1101/2020.03.02.974022 

  9. Fontanelli, O., & Mansilla, R. (2020). Modeling the Popularity of Twitter Hashtags with Master Equations. arXiv preprint, https://arxiv.org/pdf/2003.02672.pdf 

  10. Young, N. E., Jarnevich, C. S., Sofaer, H. R., Pearse, I., Sullivan, J., Engelstad, P., & Stohlgren, T. J. (2020). A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales. PLOS ONE, 15(3), e0229253. https://doi.org/10.1371/journal.pone.0229253 

  11. Allen, D., & Kim, A. Y. (2020). A permutation test and spatial cross-validation approach to assess models of interspecific competition between trees. PLOS ONE, 15(3), e0229930. https://doi.org/10.1371/journal.pone.0229930 

  12. Borgoni, R., Gilardi, A., & Zappa, D. (2020). Assessing the Risk of Car Crashes in Road Networks. Social Indicators Research. https://doi.org/10.1007/s11205-020-02295-x 

  13. Dadi, B. B. (2020). Modeling malaria cases associated with environmental risk factors in Ethiopia using geographically weighted regression (Doctoral dissertation). http://repositori.uji.es/xmlui/bitstream/handle/10234/187004/TFM_Berhanu.pdf?sequence=1&isAllowed=y 

  14. Pruchnik, P. (2020). Identification of Trends in the Polish Media on the Example of the Quarterly Studia Medioznawcze The Use of Big Data Tools. Media Studies, 80(1). http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.desklight-e79ed2c7-fd7d-4a91-8895-c322743c8f48/c/04_Pruchnik_EN.pdf 

  15. Pavlovich, S. S., Darling, T., Hume, A. J., Davey, R. A., Feng, F., Mühlberger, E., & Kepler, T. B. (2020). Egyptian Rousette IFN-ω Subtypes Elicit Distinct Antiviral Effects and Transcriptional Responses in Conspecific Cells. Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.00435 

  16. Hagen, L., Neely, S., Keller, T. E., Scharf, R., & Vasquez, F. E. (2020). Rise of the Machines? Examining the Influence of Social Bots on a Political Discussion Network. Social Science Computer Review, 089443932090819. https://doi.org/10.1177/0894439320908190 

  17. Chakraborty, T. (2020). Multi-scale assessment of drought-induced forest dieback (Doctoral dissertation). https://run.unl.pt/bitstream/10362/94403/1/TGEO0243.pdf 

  18. Salgado, D., & Oancea, B. (2020). On new data sources for the production of official statistics. arXiv preprint https://arxiv.org/pdf/2003.06797.pdf 

  19. Jézéquel, C., Tedesco, P. A., Bigorne, R., Maldonado-Ocampo, J. A., Ortega, H., Hidalgo, M., … Oberdorff, T. (2020). A database of freshwater fish species of the Amazon Basin. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0436-4 

  20. Chapman, A., Belbin, L., Zermoglio, P., Wieczorek, J., Morris, P., Nicholls, M., … Schigel, D. (2020). Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards, 4. https://doi.org/10.3897/biss.4.50889 

  21. Hamilton, L. M., & Lahne, J. (2020). Fast and automated sensory analysis: Using natural language processing for descriptive lexicon development. Food Quality and Preference, 83, 103926. https://doi.org/10.1016/j.foodqual.2020.103926 

  22. Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. Computers & Geosciences, 139, 104473. https://doi.org/10.1016/j.cageo.2020.104473 

  23. Moran, N. P., Sánchez-Tójar, A., Schielzeth, H., & Reinhold, K. (2020). Poor condition promotes high-risk behaviours but context-dependency is key: A systematic review and meta-analysis. Ecorxiv preprint. https://ecoevorxiv.org/xsehd/ 

  24. Lindner, M., Gilhooley, M. J., Palumaa, T., Morton, A. J., Hughes, S., & Hankins, M. W. (2020). Expression and Localization of Kcne2 in the Vertebrate Retina. Investigative Opthalmology & Visual Science, 61(3), 33. https://doi.org/10.1167/iovs.61.3.33 

  25. Wang, Y., Zhang, X., Song, Q., Hou, Y., Liu, J., Sun, Y., & Wang, P. (2020). Characterization of the chromatin accessibility in an Alzheimer’s disease (AD) mouse model. Alzheimer’s Research & Therapy, 12(1). https://doi.org/10.1186/s13195-020-00598-2 

  26. Hoffman, M. M., Zylla, J. S., Bhattacharya, S., Calar, K., Hartman, T. W., Bhardwaj, R. D., … Messerli, S. M. (2020). Analysis of Dual Class I Histone Deacetylase and Lysine Demethylase Inhibitor Domatinostat (4SC-202) on Growth and Cellular and Genomic Landscape of Atypical Teratoid/Rhabdoid. Cancers, 12(3), 756. https://doi.org/10.3390/cancers12030756 

  27. Bastide, P., Ho, L. S. T., Baele, G., Lemey, P., & Suchard, M. A. (2020). Efficient Bayesian Inference of General Gaussian Models on Large Phylogenetic Trees. arXiv preprint arXiv:2003.10336. https://arxiv.org/pdf/2003.10336