Posts Tagged ‘ programming ’

Computational Statistics

May 9, 2010
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Computational Statistics

Do not resort to Monte Carlo methods unnecessarily. When I received this 2009 Springer-Verlag book, Computational Statistics, by James Gentle a while ago, I briefly took a look at the table of contents and decided to have a better look later… Now that I have gone through the whole book, I can write a short

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hash-2.0.0

April 30, 2010
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hash-2.0.0

Come see my talk on hashes in R at useR! 2010. (http://user2010.org/) July 20-23 National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA

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R : NA vs. NULL

April 25, 2010
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R : NA vs. NULL

The R language has two closely related NULL-like values, NA and NULL ... Both are used to represent missing or undefined values. This has lead to much confusion.

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Doing Maximum Likelihood Estimation by Hand in R

April 21, 2010
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Doing Maximum Likelihood Estimation by Hand in R

Lately I’ve been writing maximum likelihood estimation code by hand for some economic models that I’m working with. It’s actually a fairly simple task, so I thought that I would write up the basic approach in case there are readers who haven’t built a generic estimation system before. First, let’s start with a toy example

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Experiments with igraph

April 21, 2010
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Experiments with igraph

Networks – social and biological – are all the rage, just now. Indeed, a recent entry at Duncan’s QOTD described the “hairball” network representation as the dominant cultural icon in molecular biology. I’ve not had occasion to explore networks “professionally”, but have always been fascinated by both networks and the tools used to analyse them.

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Getting your web application and R(Apache) to talk to each other

April 19, 2010
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Getting your web application and R(Apache) to talk to each other

Here’s the situation. Web applications, built using a framework (e.g. Rails, Django) are great for fetching data from a database and rendering it. They’re not so great for crunching and charting the data. Conversely, R is great for crunching and charting, but doesn’t make for a great web application. The idea then, is to let

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R is an Epic Fail?

April 15, 2010
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I came across this blog post just now: The Next Big Thing, and of course these words caught my attention: However, for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On

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I’d be more than happy with the unlinked data web

April 14, 2010
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I’d be more than happy with the unlinked data web

Visit this URL and you’ll find a perfectly-formatted CSV file containing information about recent earthquakes. A nice feature of R is the ability to slurp such a URL straight into a data frame: quakes <- read.csv("http://neic.usgs.gov/neis/gis/qed.asc", header = T) colnames(quakes) # "Date" "TimeUTC" "Latitude" "Longitude" "Magnitude" "Depth" # number of recent quakes nrow(quakes) #

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Plotting “time of day” data using ggplot2

April 14, 2010
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Plotting “time of day” data using ggplot2

William asks: How can I make a graph that looks like this, “tweet density” style, showing time intervals? He then helpfully describes his input data: a CSV file with headers “time started, time finished, date”. Here’s a simple CSV file, tasks.csv: task,date,start,end task1,2010-03-05,09:00:00,13:00:00 task2,2010-03-06,10:00:00,15:00:00 task3,2010-03-06,11:00:00,18:00:00 task4,2010-03-07,08:00:00,11:00:00 task5,2010-03-08,14:00:00,17:00:00 task6,2010-03-09,12:00:00,16:00:00 task7,2010-03-10,14:00:00,19:00:00 task8,2010-03-11,09:30:00,13:30:00 Read into R, calculate the

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Seamless R Extensions using Rcpp and RInside

April 7, 2010
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I just added a new video to the R repository, and this one comes from the Los Angeles R Meetup. The folks in LA were fortunate enough to have Dirk Eddelbuettel—renowned R expert and StackOverflow super-user—discuss his joint work with Romain François for interfacing C++ and R code using the Rcpp package. For those

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