I'm a big fan of Python for data analysis, but even I get curious about what else is available. R has long been the go-to ...

Overview: Data analysis software makes sense of large amounts of information quickly.Tools like R, Python, and SPSS simplify research and statistical studies.Vi ...

In studies on HSCT Kaplan–Meier (KM) estimates of survival curves and Cox proportional hazard models are widely used to describe survival trends and identify significant prognostic factors. All these ...

Simply put by one of its staunchest advocates, "R is the most powerful statistical computing language on the planet; there is no statistical equation that cannot be calculated in R." Beyond "just" a ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several ...

The RNA-seq platform 1,2 addresses a multitude of applications, including relative expression analyses, alternative splicing, discovery of novel transcripts and isoforms, RNA editing, allele-specific ...

The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis — plus a few miscellaneous tasks tossed in. The package names in the table are clickable if ...

Once you’ve installed and configured R to your liking, it’s time to start using it to work with data. Yes, you can type your data directly into R’s interactive console. But for any kind of serious ...