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This is a quick reference for the R modeling syntax and associated packages. While it covers a lot of ground, it is not meant to be exhaustive, but rather, it provides an easy reference for those new to R, someone trying out an unfamiliar (but otherwise common) technique, or those just interested in a comparison to similar approaches in other environments. It can get you quickly started with many common models and extensions.

Models covered:

GLM,other distributions andcategorical outcomes,regularized models,mixed models,additive models,survival analysis,survey weighting,PCA/FA,SEM,mixture models/cluster analysis,time series,spatial models,graphical models,machine learning,Bayesian analysis,text analysis, dealing withmissing data.

In addition, notable packages and recommended readings are provided.

You can find the current documenthere.