# Monthly Archives: October 2011

## Covariance structures

October 26, 2011
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$Covariance structures$

In most mixed linear model packages (e.g. asreml, lme4, nlme, etc) one needs to specify only the model equation (the bit that looks like y ~ factors...) when fitting simple models. We explicitly say nothing about the covariances that complete … Continue reading →

## Two-sex demographic models in R

October 26, 2011
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Tom Miller (a prof here at Rice) and Brian Inouye have a paper out in Ecology (paper, appendices) that confronts two-sex models of dispersal with empirical data.They conducted the first confrontation of two-sex demographic models with empirical data on...

## Controlling multiple risk measures during construction of efficient frontier

October 26, 2011
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In the last few posts I introduced Maximum Loss, Mean-Absolute Deviation, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures. These risk measures can be formulated as linear constraints and thus can be combined with each other to control multiple risk measures during construction of efficient frontier. Let’s examine efficient frontiers computed

## PAWL package on CRAN

October 26, 2011
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The PAWL package (which I talked about there, and which implements the parallel adaptive Wang-Landau algorithm and adaptive Metropolis-Hastings for comparison) is now on CRAN! http://cran.r-project.org/web/packages/PAWL/index.html which means that within R you can easily install it by typing install.packages("PAWL") Isn’t that amazing? It’s just amazing. Kudos to the CRAN team for their quickness and their

## New features in R-bloggers.com

October 26, 2011
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Hello dear R community, In the past few months I have rolled out a bunch of new features to R-bloggers, and I wanted to raise awareness to them.  Please consider giving some of these a try and leave me any feedback that you have (by leaving a comment on this post): Comments – it is now possible to leave comments in...

## Batch Processing vs. Interactive Sessions

October 26, 2011
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We introduced batch processing 3 weeks ago. Many people asked about differences and benefits of batch processing or interactive sessions. Lets start with the definitions: Batch Processing / Batch Jobs: Batch processing is the execution of a series of programs or only one task on a computer environment without manual intervention. All data and commands

## Machine Learning Ex 5.2 – Regularized Logistic Regression

October 25, 2011
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Now we move on to the second part of the Exercise 5.2, which requires to implement regularized logistic regression using Newton's Method. Plot the data:

## treebase package on cran

October 25, 2011
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My treebase package is now up on the CRAN repository. (Source code is up, the binaries should appear soon). Here’s a few introductory examples to illustrate some of the functionality of the package. Thanks in part to new data deposition requirements at journals such as Evolution, Am Nat, and Sys Bio, and data management plan

## The Psychology of Music and the ‘tuneR’ Package

October 25, 2011
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Introduction This semester I’m TA’ing a course on the Psychology of Music taught by Phil Johnson-Laird. It’s been a great course to teach because (i) so much of the material is new to me and (ii) because the study of the psychology of music brings together so many of the intellectual tools I enjoy, including

## "Anyone planning to work with Big Data ought to learn Hadoop and R"

October 25, 2011
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Dan Woods at Forbes interviewed LinkedIn's Daniel Tunkelang about the rise of data science and on building data science teams. When asked how students today should prepare themselves to be data scientists, Tunkelang gives some good advice: When we built the data science team at LinkedIn a few years ago, we looked for raw talent, assuming that smart people...