Publications by ntguardian
How Should I Organize My R Research Projects?
My formal training in computer programming consists of two R programming labs required by my first statistics classes, and some JavaScript and database training. That’s about it. Most of my programming knowledge is self-taught.1 For a researcher who does a lot of programming but doesn’t consider programming to be the job, that’s fine… up ...
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Time to Accept It: publishing in the Journal of Statistical Software
I’m reblogging this article mostly for myself. If you’ve been following my blog, you’ll see that recently I published an article on organizing R code that mentioned using packages to organize that code. One of the advantages of doing so is that the work you’ve done is easily distributed. If the methods are novel in some way, you may even ...
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Evaluating Olive McBride with the Arkham Horror LCG Chaos Bag Simulator in R
(If you care, there may be spoilers in this post.) Introduction I love Arkham Horror; The Card Game. I love it more than I really should; it’s ridiculously fun. It’s a cooperative card game where you build a deck representing a character in the Cthulhu mythos universe, and with that deck you play scenarios in a narrative campaign1 where you g...
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Materials for Teaching Applied Statistics
Today is the first day of the new academic year at the University of Utah. This semester I am teaching MATH 3070: Applied Statistics I, the fourth time I’ve taught this course. This is the first semester where I feel like I actually am fully, 100% prepared to teach this class. I’ve taught MATH 1070: Introduction to Statistical Inference many...
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Naïve Numerical Sums in R
Introduction The Kolmogorov distribution (which I call ) is as follows: There is no known simpler form and we have to work with this sum as it is. This is an infinite sum. How can we compute the value of this infinite sum numerically? Naïvely we can do the following: summand <- function(x, k) sqrt(2 * pi)/x * exp(-(2 * k - 1)^2 * pi^2/(8 * x^2)...
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Announcing MCHT: An R Package for Bootstrap and Monte Carlo Hypothesis Testing
I am very excited to announce my first (public) package (and the second package I’ve ever written, the first being unannounced until the accompanying paper is accepted). That package is MCHT, a package for bootstrap and Monte Carlo hypothesis testing, currently available on GitHub. This will be the first of a series of blog posts introducing t...
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MCHT, Closures, and R Environments: Making MCHTest Objects Self-Contained
Introduction Last week I announced the first release of MCHT, an R package that facilitates bootstrap and Monte Carlo hypothesis testing. In this article, I will elaborate on some important technical details about making MCHTest objects, explaining in the process how closures and R environments work. To recap, last week I made a basic MCHTest-cl...
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Maximized Monte Carlo Testing with MCHT
Introduction I introduced MCHT two weeks ago and presented it as a package for Monte Carlo and boostrap hypothesis testing. Last week, I delved into important technical details and showed how to make self-contained MCHTest objects that don’t suffer side effects from changes in the global namespace. In this article I show how to perform maximize...
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Bootstrap Testing with MCHT
Introduction Now that we’ve seen MCHT basics, how to make MCHTest() objects self-contained, and maximized Monte Carlo (MMC) testing with MCHT, let’s now talk about bootstrap testing. Not much is different when we’re doing bootstrap testing; the main difference is that the replicates used to generate test statistics depend on the data we fee...
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Beyond Univariate, Single-Sample Data with MCHT
Introduction I’ve spent the past few weeks writing about MCHT, my new package for Monte Carlo and bootstrap hypothesis testing. After discussing how to use MCHT safely, I discussed how to use it for maximized Monte Carlo (MMC) testing, then bootstrap testing. One may think I’ve said all I want to say about the package, but in truth, I’ve on...
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