Notes

SURFACE 51 is a Urbana-Champaign graphic and web design company. You can check out their portfolio here.

Jon Kleinberg is visiting UIUC today and giving a talk “Decentralized Search, Cascading Behavior, and the Structure of On-Line Communities” in the Age of Networks seminar series. The abstract of his talk today: The rise of large-scale information networks has provided us with systems that are simultaneously social and technological in nature, and in which the human interactions that unfold can be recorded and studied in extraordinary detail. Against this backdrop, a significant amount of recent work has focused on the development of mathematical models that capture some of the qualitative properties observed in large-scale network data; such models have the potential to help us reason, at a general level, about the ways in which large networks are organized, and about the ways in which abstract models can capture the processes we observe. ...

The package e1071 for R is an interesting add on to your list of R packages. It includes functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, independent component analysis, and more.

Zotero has released a first integration plugin for MS Word. Pretty useful if you need to move your bibtex into a Word document.

There are, at least :), two ways to compute the principal component analysis of a data set in R. The first one is from scratch computing eigenvectors and eigenvalues. It works as follows # # From scratch # cbind(1:10,1:10 + 0.25*rnorm(10)) -> myData myData - apply(myData,2,mean) -> myDataZM cov(myDataZM) -> cvm eigen(cvm,TRUE) -> eCvm t(eCvm$vector%*%t(myDataZM)) -> newMyData This simple code just transforms the data to align it with the principal components obtained. Of couse, the second way to compute them is using some of the functions that R provides in the stats package. ...