July 2015

htmltab v.0.6.0

July 23, 2015 | Christian Rubba

The next version of the htmltab package has just been released on CRAN and GitHub. The goal behind htmltab is to make the collection of structured information from HTML tables as easy and painless as possible (read about the package here and here). The most recent update got rid of ... [Read more...]

Stan 2.7 (CRAN, variational inference, and much much more)

July 22, 2015 | Bob Carpenter

Stan 2.7 is now available for all interfaces. As usual, everything you need can be found starting from the Stan home page: http://mc-stan.org/ Highlights RStan is on CRAN!(1) Variational Inference in CmdStan!!(2) Two new Stan developers!!!  A whole new logo!!!!  Math library with autodiff now available in its own ... [Read more...]

Le Monde puzzle [#920]

July 22, 2015 | xi'an

A puzzling Le Monde mathematical puzzle (or blame the heat wave): A pocket calculator with ten keys (0,1,…,9) starts with a random digit n between 0 and 9. A number on the screen can then be modified into another number by two rules: 1. pressing k changes the k-th digit v whenever it exists ...
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Count data: To Log or Not To Log

July 22, 2015 | grumble10

Count data are widely collected in ecology, for example when one count the number of birds or the number of flowers. These data follow naturally a Poisson or negative binomial distribution and are therefore sometime tricky to fit with standard LMs. A traditional approach has been to log-transform such data ...
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New caret Version (6.0-52)

July 22, 2015 | Max Kuhn

A new version of caret (6.0-52) is on CRAN. Here is the news file but the Cliff Notes are: sub-sampling for class imbalances is now integrated with train and is used inside of standard resampling. There are four methods available right now: up- and... [Read more...]

New caret Version (6.0-52)

July 22, 2015 | Max Kuhn

A new version of caret (6.0-52) is on CRAN. Here is the news file but the Cliff Notes are: sub-sampling for class imbalances is now integrated with train and is used inside of standard resampling. There are four methods available right now: up- and... [Read more...]

Doodling With 3d Animated Charts in R

July 22, 2015 | Tony Hirst

Doodling with some Gapminder data on child mortality and GDP per capita in PPP$, I wondered whether a 3d plot of the data over the time would show different trajectories over time for different countries, perhaps showing different development pathways over time. Here are a couple of quick sketches, generated ...
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"Models, Models Everywhere!" Brought to You by R

July 21, 2015 | Joel Cadwell

Statistical software packages sell solutions. If you go to the home page for SAS, they will tell you upfront that they sell products and solutions. They link both together under the first tab just below "The Power to Know" mantra. SPSS separates product and solution into separate tabs, but places ... [Read more...]

A Tutorial on Loops in R – Usage and Alternatives

July 21, 2015 | DataCamp

Introduction In this easy-to-follow R tutorial on loops we will examine the constructs available in R for looping, and how to make use of R’s vectorization feature to perform your looping tasks more efficiently. We will present a few looping examples; then criticize and deprecate these in favor of ... [Read more...]

A Statistical Analysis of the LearnedLeague Trivia Competition

July 21, 2015 | Todd Schneider

LearnedLeague bills itself as “the greatest web-based trivia league in all of civilized earth.” Having been fortunate enough to partake in the past 3 seasons, I’m inclined to agree. LearnedLeague players, known as “LLamas”, answer trivia questions drawn from 18 assorted categories, and one of the many neat things about LearnedLeague ... [Read more...]

Choosing a Classifier

July 21, 2015 | arthur charpentier

In order to illustrate the problem of chosing a classification model consider some simulated data, __ n = 500 __ set.seed(1) __ X = rnorm(n) __ ma = 10-(X+1.5)^2*2 __ mb = -10+(X-1.5)^2*2 __ M = cbind(ma,mb) __ set.seed(1) __ Z = sample(1:2,size=n,replace=TRUE) __ Y = ma*(Z==1)+mb*(Z==2)+rnorm(n)*5 __ df = data.frame(Z=...
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Parametric Inference: Karlin-Rubin Theorem

July 20, 2015 | Al Asaad

A family of pdfs or pmfs $\{g(t|\theta):\theta\in\Theta\}$ for a univariate random variable $T$ with real-valued parameter $\theta$ has a monotone likelihood ratio (MLR) if, for every $\theta_2__\theta_1$, $g(t|\theta_2)/g(t|\theta_1)$ is a monotone (nonincreasing or nondecreasing) function of $t$ on $\{t:...
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