How to fit and use the components model. Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series Variance targeting in garch estimation The model The components model (created by Engle and Lee) generally works better than the more common garch(1,1) model. Some hints about … Continue reading...
I will make a confession. This past summer, I didn’t spend my spare time watching relentlessly addicting TV shows nor clubbing in San Francisco. Instead, I checked out figures. No, not the sort of figures you’re probably thinking about. The ones that are included in research papers and have the potential to be beautiful works of 
Version 0.3.0 of Shiny is now available on CRAN. This version of Shiny has several new features and bug fixes. Some of the changes are under the hood: for example, Shiny now uses a more efficient algorithm for scheduling the execution of reactive functions. There are also some user-facing changes: for example, the new runGitHub() 
Having posted some code to Git a few days ago and having been wholly dissatisfied with it, I began to do what I often do with code I don’t like. I started re-writing it bigger and weirder and more philosophically pure. Part of this search for Platonic code lead me to explore object oriented programming 
In this article we’ll get up and running on the Semantic Web in less than 5 minutes using SPARQL with R. We’ll begin with a brief introduction to the Semantic Web then cover some simple steps for downloading and analyzing government data via a SPARQL query with the SPARQL R package. What is the Semantic
The post SPARQL...
In case you missed them, here are some articles from December of particular interest to R users. The blog is.R ran an excellent series of R tips and applications in December, with posts including working with Stata files, working with graphs and networks, and text analysis. Kohske Takahashi provides R scripts to create a collection of optical illusions. Highlights...
Restricted Boltzmann Machines (RBMs) are an unsupervised learning method (like principal components). An RBM is a probabilistic and undirected graphical model. They are becoming more popular in machine learning due to recent success in training them with contrastive divergence. They have been proven useful in collaborative filtering, being one of the most successful methods...
I really enjoyed tonight’s DC R Meetup, presented by the prolific Anthony Damico. I’ve met Anthony before to discuss whether the Census Bureau could either… publish R-readable … Continue reading →
One of the many things I keep avoiding is statistics. I’ve never really been convinced about the 5% significance level thing; as far as I can tell, hardly anything that’s interesting normally distributes; all the counting that’s involved just confuses me; and I never really got to grips with confidently combining probabilities. I find a 
As mentioned in the Appendix of Modern Actuarial Risk Theory, “R (and S) is the ‘lingua franca’ of data analysis and statistical computing, used in academia, climate research, computer science, bioinformatics, pharmaceutical industry, customer analytics, data mining, finance and by some insurers. Apart from being stable, fast, always up-to-date and very versatile, the chief advantage of R is that...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
