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Is your statistical software correct?
Performing modern statistical research is almost impossible without the aid of software. Many of us use statistical software to one degree or another – from small analyses in Microsoft Excel through to R or Python Scripts on our laptops right up to large machine learning pipelines in the cloud.
How can we be sure that our computational results are correct when we employ these techniques? In this talk, I will discuss what can go wrong and strategies we can employ to increase the probability that our computational research gives the right answer.
Putting the R into Reproducible Research
R and its ecosystem of packages offers a wide variety of statistical and graphical techniques and is increasing in popularity as the tool of choice for data analysis in academia.In addition to its powerful analytical features, the R ecosystem provides a large number of tools and conventions to help support more open, robust and reproducible research. This includes tools for managing research projects, building robust analysis workflows, documenting data and code, testing code and disseminating and sharing analyses.In this talk we’ll take a whistle-stop tour of the breadth of available tools, demonstrating the ways R and the Rstudio integrated development environment can be used to underpin more open reproducible research and facilitate best practice
The committee and our speakers will stay online for a few more minutes to wrap up any discussions
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