Blog Not Down

It has been one year since my last article, and here is a quick post indicating that my blog is not down. Instead, it has a new look thanks to blogdown. Yes, pun intended. :-) blogdown, mostly written by Yihui, is an R package that can help you rapidly create a static blog or website. The package name has nothing to do with the status of a website (as in “the server is down”), but rather follows the convention of other Markdown-based packages such as rmarkdown and bookdown.
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A Quick View of Recommender System The main task of recommender system is to predict unknown entries in the rating matrix based on observed values, as is shown in the table below: Each cell with number in it is the rating given by some user on a specific item, while those marked with question marks are unknown ratings that need to be predicted. In some other literatures, this problem may be named collaborative filtering, matrix completion, matrix recovery, etc.
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Introduction I have seen several conversations in Rcpp-devel mailing list asking how to compute numerical integration or optimization in Rcpp. While R in fact has the functions Rdqags, Rdqagi, nmmin, vmmin etc. in its API to accomplish such tasks, it is not so straightforward to use them with Rcpp. For my own research projects I need to do a lot of numerical integration, root finding and optimization, so to make my life a little bit easier, I just created the RcppNumerical package that simplifies these procedures.
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In January 2016, I was honored to receive an “Honorable Mention” of the John Chambers Award 2016. This article was written for R-bloggers, whose builder, Tal Galili, kindly invited me to write an introduction to the rARPACK package. A Short Story of rARPACK Eigenvalue decomposition is a commonly used technique in numerous statistical problems. For example, principal component analysis (PCA) basically conducts eigenvalue decomposition on the sample covariance of a data matrix: the eigenvalues are the component variances, and eigenvectors are the variable loadings.
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This semester I’m taking a course in big data computing using Scala/Spark, and we are asked to finish a course project related to big data analysis. Since statistical modeling heavily relies on linear algebra, I investigated some existing libraries in Scala/Java that deal with matrix and linear algebra algorithms. 1. Set-up Scala/Java libraries are usually distributed as *.jar files. To use them in Scala, we can create a directory to hold them and set up the environment variable to let Scala know about this path.
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This is a pretty old topic in R graphics. A classical article in R NEWS, Non-standard fonts in PostScript and PDF graphics, describes how to use and embed system fonts in the PDF/PostScript device. More recently, Winston Chang developed the extrafont package, which makes the procedure much easier. A useful introduction article can be found in the readme page of extrafont, and also from the Revolution blog. Now, we have another choice: the showtext package.
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Yixuan's Blog

Statistics, Programming, and …?