Reason 8 Powerful statistical methods at your fingertips
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Many simple stats are built into R (glm(), chisq.test() and many more). No additional
packages needed! For more advanced analyses there are often multiple packages. Best to ask
a domain expert what they use (or Google). Here are just a few examples:
- GAM, HLM, Mixture Models…
- SEM with
{lavaan} - Robust stats with {WRS2}
- Time series with
{forecast}{zoo},{xts} - Bayesian Statistics with
{brms},{rstanarm},{rstan} - Text mining with
{tidytext} - Machine learning with
{tidymodels} - Network analysis with
{tidygraph}or{igraph} - Meta analysis with
{metafor}and{rmeta} - Introduction to Statistical Learning – A classic introduction to machine learning using R.
{tidymodels}– package for a more modern machine learning toolbelt in R
There are too many things to mention. The CRAN task views can be a good start.