![]() In an ideal world, everything we use would be cited, but with word and reference limits and editors less aware of the importance of citing software, it’s often hard to justify citing everything in a manuscript. connecting to OSF, the Open Science Framework),īut not necessarily for the analysis specifically. ![]() Or packages like osfr which are used as part of the scientific process The tricky bit is packages that are used generally, like data munging packages, Or if you used tidyhydat to retrieve hydrology data, definitely cite those packages). if you used magick to process images before analysis, I always advise citing statistical packages, no question, and any package that is specific to a scientific domain or methodology (i.e. This is a tricky question, and to be honest, I’m not really sure of the best answer,Īnd sometimes it may depend on many factors. R packages have made my life so much easier, and it makes me happy to know that at least I can publicly acknowledge the hard work of the developers by citing them in my manuscripts.Ī question I often hear is, “Okay, I understand I should cite R packages, but do I cite them all?”. I know that every citation I get for my weathercan package warms my heart □ (and helps my CV)! Of software dependencies (see below, Reproducible science).Ĭiting developer’s work is also a way of showing gratitude and thanks for (often) unpaid hard work. If you’re interested in ensuring your analysis is reproducible (as opposed to your research being repeatable), you should look into more rigorous ways of keeping track Note: Repeatability isn’t the same as reproducibility. Including versions is also really important as a way of recording the context of your manuscript It not only helps other scientists understand and repeat your work, but helps share important and useful tools. Software (like R), R packages, and their versions are important information to include in a manuscript. The lack of citations of software in scientific publications has become a problem to the extent that working groups, such as FORCE11 Software Citation Implementation Working Group, have been created to establish guidelines 2 and standards 3 for citing software. If it isn’t clear how you did a thing or how/where you got your data, your work isn’t repeatable. It’s important that the developers get credit for their work 1. If we want to ensure that high-quality open software continues, This makes it hard to prioritize work on open software projects. Without citations, most scientists don’t get credit for their work. They may be developing packages in their own time, or are trying to fit it into a busy schedule. ![]() Many developers of R packages for science are themselves scientists. It’s extremely important to cite both R and R packages for several reasons: So here’s a short primer on why and how to get started! □ Many scientists don’t know that they should be citing R packages let alone R,Īnd, if they do know, they often struggle with how. That they often forget or aren’t aware of the next steps. Or remembering where to put the comma, they’re so grateful to actual have an analysis, I find that after all their struggles of dealing with dates, students)Īs well as more established scientists, new to R. ![]() Recent observations, reducing tendency to overshoot.I teach R to a lot of scientists, those that are new to science (i.e. HMA a WMA of the difference of two other WMAs, making it veryĪLMA inspired by Gaussian filters. Higher (lower) values of w will cause VMA VMA calculate a variable-length moving average based on the absolute VWMA and VWAP calculate the volume-weighted moving average (n-1)/2 periods (default) to minimize the cumulative effect. Observations, but attempts to remove lag by subtracting data prior to ZLEMA is similar to an EMA, as it gives more weight to recent Length of x, the WMA will use the values of wts as weights.ĭEMA is calculated as: DEMA = (1 + v) * EMA(x,n) -ĮMA(EMA(x,n),n) * v (with the corresponding wilder and ratioĮVWMA uses volume to define the period of the MA. WMA is similar to an EMA, but with linear weighting if the length of SMA calculates the arithmetic mean of the series over the pastĮMA calculates an exponentially-weighted mean, giving more weight to As the indicator receives more data, its outputīecomes more stable. ![]() EMA, DEMA, EVWMA, etc.) areĬalculated using the indicators' own previous values, and are therefore ![]()
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