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.
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.
Dr. Hadley Wickham is the Chief Scientist of RStudio and Assistant Professor of Statistics at Rice University. He is the developer of the famous R package ggplot2 for data visualization and the author of many other widely used packages like plyr and reshape2. On Sep 13, 2013 he gave a talk at Department of Statistics, Purdue University, and later I (Yixuan) had a conversation with him (Hadley), talking about his own experience and interest on data visualization, data tidying, R programming and other related topics.