I often need to analyze and model very wide data (variables >>>samples), and because of this I gravitate to robust yet relatively simple methods. In my opinion partial least squares (PLS) is a particular useful algorithm. Simply put, PLS is an extension of principal components analysis (PCA), a non-supervised method to maximizing variance explained in X, which instead maximizes the covariance between X and Y(s). Orthogonal partial least squares (OPLS) is a variant of PLS which uses orthogonal signal correction to maximize the explained covariance between X and Y on the first latent variable, and components >1 capture variance in X which is orthogonal (or unrelated) to Y.
I was extremely impressed with ease of using knitr and generating markdown from code using RStudio. A big thank you to Yihui Xie and the RStudio developers (Joe Cheng). This is an amazing capability which I will make much more use of in the future!
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