Blog Archives

Mysteriously Slow sample

July 28, 2018
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Mysteriously Slow sample

Hi everyone, I'm at JSM 2018 right now, so feel free to drop by my session or drop by in the halls! Just give me a tweet! Back to the meat-and-potatoes of this post. A while ago I was running good old sample and comparing its performance to my lpm2_kdtree function in the BalancedSampling package (Grafström and Lisic,...

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Making Your .C Less NOTEworthy

January 12, 2018
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Making Your .C Less NOTEworthy

If you are a package maintainer, you may have noticed the following new notes from your code checks: Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’ If you are using Rcpp you can easily fix this by refreshing the auto-generated function registration. However, if you have a lot of C code that uses the .C() interface, then you need to...

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Making Your .C Less NOTEworthy

January 11, 2018
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Making Your .C Less NOTEworthy

If you are a package maintainer, you may have noticed the following new notes from your code checks: Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’ If you are using Rcpp you can easily fix this by refreshing the auto-generated function registration. However, if you have a lot of C code that uses the .C() interface, then you need to...

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The Statistician’s Apprentice: An Introduction to the SWP Operator

December 13, 2016
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The Statistician’s Apprentice: An Introduction to the SWP Operator

The sweep operator as defined in (Dempster, 1969), commonly referred to as the SWP operator, is a useful tool for a computational statistician working with covariance matrices. In particular, the SWP operator allows a statistician to quickly regress all variables against one specified variable, obtaining OLS estimates for regression coefficients and variances in a single application. Subsequent...

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The Statistician’s Apprentice: An Introduction to the SWP Operator

December 12, 2016
By
The Statistician’s Apprentice: An Introduction to the SWP Operator

The sweep operator as defined in (Dempster, 1969), commonly referred to as the SWP operator, is a useful tool for a computational statistician working with covariance matrices. In particular, the SWP operator allows a statistician to quickly regress all variables against one specified variable, obtaining OLS estimates for regression coefficients and variances in a single application. Subsequent...

Read more »

meanShiftR

August 27, 2016
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meanShiftR

In this blog post, I will be introducing the meanShiftR package. meanShiftR is a rewrite of my original mean shift R package from 2013, based on the Fast Library for Approximate Nearest Neighbors (FLANN). The meanShiftR package is focused on providing to R users the most computationally efficient mean shift implementations available in the literature. This includes approximations to the...

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meanShiftR

August 27, 2016
By
meanShiftR

In this blog post, I will be introducing the meanShiftR package. meanShiftR is a rewrite of my original mean shift R package from 2013, based on the Fast Library for Approximate Nearest Neighbors (FLANN). The meanShiftR package is focused on providing to R users the most computationally efficient mean shift implementations available in the literature. This includes approximations to the...

Read more »

Elizabeth!

August 6, 2016
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Elizabeth!

Over the last few months I have been spending my nights taking care of my newly born second daughter. Keeping me company during the sleepless wee hours of the morning was the Reconcilable Differences Podcast. In episode 17 of this podcast, It's Devastating, there was an open question placed by John Siracusa with regard to how baby names change...

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Elizabeth!

August 6, 2016
By
Elizabeth!

Over the last few months I have been spending my nights taking care of my newly born second daughter. Keeping me company during the sleepless wee hours of the morning was the Reconcilable Differences Podcast. In episode 17 of this podcast, It's Devastating, there was an open question placed by John Siracusa with regard to how baby names change...

Read more »

Kernels for everyone!

August 1, 2016
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Kernels for everyone!

During my dissertation, I spent a lot of time working on spatial kernel estimates. Where spatial kernel estimates are defined as a convolution of a spatial suppport , A simple example of this estimate is a Gaussian filter or blur in more common parlance. In the Guassian filter, is the normal density function , with the location parameter and...

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