BioRssay: an R package for analyses of bioassays and probit graphs
Parasites and Vectors, ISSN: 1756-3305, Vol: 15, Issue: 1, Page: 35
2022
- 20Citations
- 31Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations20
- Citation Indexes20
- 20
- Captures31
- Readers31
- 31
Article Description
Dose–response relationships reflect the effects of a substance on organisms, and are widely used in broad research areas, from medicine and physiology, to vector control and pest management in agronomy. Furthermore, reporting on the response of organisms to stressors is an essential component of many public policies (e.g. public health, environment), and assessment of xenobiotic responses is an integral part of World Health Organization recommendations. Building upon an R script that we previously made available, and considering its popularity, we have now developed a software package in the R environment, BioRssay, to efficiently analyze dose–response relationships. It has more user-friendly functions and more flexibility, and proposes an easy interpretation of the results. The functions in the BioRssay package are built on robust statistical analyses to compare the dose/exposure–response of various bioassays and effectively visualize them in probit-graphs. Graphical Abstract: [Figure not available: see fulltext.]
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85123496119&origin=inward; http://dx.doi.org/10.1186/s13071-021-05146-x; http://www.ncbi.nlm.nih.gov/pubmed/35073988; https://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-021-05146-x; https://dx.doi.org/10.1186/s13071-021-05146-x
Springer Science and Business Media LLC
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