November 2016

Earthquake energy over time

November 18, 2016 | Peter's stats stuff - R

Disclaimer on all that follows - I am not an earthquake scientist and have cobbled together this post from sources like Wikipedia, official open data, and a range of information sites. There may be mistakes and misinterpretations that follow. Energy release from earthquakes is extremely variable My last blog post ... [Read more...]

fauxpas – HTTP conditions package

November 18, 2016 | Scott Chamberlain

HTTP, or Hypertext Transfer Protocol is a protocol by which most of us interact with the web. When we do requests to a website in a browser on desktop or mobile, or get some data from a server in R, all of that is using HTTP. HTTP has a rich ... [Read more...]

Introducing trelliscopejs

November 17, 2016 | ryan hafen

I’m really excited to announce the beta release of a visualization project I’ve put a lot of work into for the past several months, trelliscopejs. trelliscopejs is an R package that brings faceted visualizations to life while plugging in to common analytical workflows like ggplot2 or the “tidyverse”. ... [Read more...]

postdoc on missing data at École Polytechnique

November 17, 2016 | xi'an

Julie Josse contacted me for advertising a postdoc position at École Polytechnique, in Palaiseau, south of Paris. “The fellowship is focusing on missing data. Interested graduates should apply as early as possible since the position will be filled when a suitable candidate is found. The Centre for Applied Mathematics (CMAP) ... [Read more...]

Discriminant Analysis for Group Separation in R

November 17, 2016 | Aaron Schlegel

The term ‘discriminant analysis’ is often used interchangeably to represent two different objectives. These objectives of discriminant analysis are: Description of group separation. Linear combinations of variables, known as discriminant functions, of the dependent variables that maximize the separation between the groups are used to identify the relative contribution of... ...
[Read more...]

Crime Analysis – Denver-Part 1

November 17, 2016 | Scott Stoltzman

Project Background As we all know, Colorado is considered one of the scariest places on earth. Denver, CO has had an enormous influx of people over the last decade and it is still ramping up. So why did I pick Denver? That’s simple, I have lived in Colorado for ... [Read more...]

Generating Data Exercises

November 17, 2016 | Mary Anne Thygesen

Let’s make data R is good a making simulated data sets. These data sets are useful for learning programming. Instead of having to spend all your time cleaning up your data you have data ready to use for learning how to program. The data that will be generated here ... [Read more...]

Notable New and Updated R packages (to October 2016)

November 17, 2016 | David Smith

As we prepare for the upcoming release of Microsoft R Open, I've been preparing the list of new and updated packages for the spotlights page. This involves scanning the CRANberries feed (with gracious thanks to Dirk Eddelbuettel) for newly-released packages and significant updates to existing ones. This is a lot ... [Read more...]

EARL Boston 2016

November 17, 2016 | Appsilon Data Science - R language

Last week our CEO, Filip Stachura, gave a speech at EARL conference in Boston titled “Rapid shiny development to blend experts knowledge into machine learning models”. EARL Boston Filip spoke to a huge number of R data scientists about how one ca... [Read more...]

Bayesian Blood

November 17, 2016 | @aschinchon

The fourth, the fifth, the minor fall and the major lift (Hallelujah, Leonard Cohen) Next problem is extracted from MacKay’s Information Theory, Inference and Learning Algorithms: Two people have left traces of their own blood at the scene of a crime. A suspect, Oliver, is tested and found to ...
[Read more...]

Inter-ocular trauma test

November 17, 2016 | Jacob Simmering

I’ve recently been thinking about the role statistics can play in answering questions. I think the it came up on the NSSD podcast a few weeks ago. Basically, problems can be divided into three classes: those that don’t need statistics because the answer is obvious (problems without much ... [Read more...]

simulation under zero measure constraints

November 16, 2016 | xi'an

A theme that comes up fairly regularly on X validated is the production of a sample with given moments, either for calibration motives or from a misunderstanding of the difference between a distribution mean and a sample average. Here are some entries on that topic: How to sample from a ... [Read more...]
1 5 6 7 8 9 15

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)