Interesante … escribir BUGS pero sin utilizar BUGS…
Write statistical models in the BUGS language from R
NIMBLE adopts and extends BUGS as a modeling language¬†and lets you program with the models you create.
Other packages that use the BUGS language are only for Markov chain Monte Carlo (MCMC). With NIMBLE, you can turn BUGS code into model objects and use them for whatever algorithm you want. That includes algorithms provided with NIMBLE and algorithms you write using nimbleFunctions. NIMBLE extends BUGS by allowing multiple parameterizations for distributions, user-written functions and distributions, and more.
Martes, 5 de julio de 2016
We had a fantastic turnout to last week’s webinar, Introduction to Microsoft R Open. If you missed it, you can watch the replay below. In the talk, I gives some background on the R language and its applications, describe the performance and reproducibility benefits of Microsoft R Open, and give a demonstration of the basics of the R language along with a more in-depth demo of producing a beautiful weather data chart with R.
Domingo, 7 de febrero de 2016
Una buena introducci√≥n.
Mi√©rcoles, 20 de enero de 2016
Os dejo un enlace interesante: This article is reposted from graphdoctor.com with the kind permission of Richard Layton.
Jueves, 23 de julio de 2015
Interesante Videotutorial para empezar a trabajar con R y Markdown:
In collaboration with Garrett Grolemund, RStudio‚Äôs teaching specialist, DataCamp has developed a new interactive course to facilitate reproducible reporting of your R analyses. R Markdown enables you to generate reports straight from your R code, documenting your works as an HTML, pdf or Microsoft document. This course is part of DataCamp‚Äôs¬†R training path, but can also be taken as a separate course.
Martes, 3 de marzo de 2015
We’ve gone through last year’s downloads and bring you our Top 10 White Papers and Webinars. Enjoy!
Top 10 Downloaded White Papers of 2014 / 2015
- Maximizing the Value of Big Data – RRE for Teradata
- Big Data Decision Trees with R
- RevoScaleR Data Step
- R is Hot
- Delivering Value from Big Data with Revolution R Enterprise and Hadoop
- Getting Started with Revolution R Enterprise
- Advanced ‘Big Data’ Analytics with R and Hadoop
- R for Web Services with RevoDeployR
- R is Still Hot – And Getting Hotter
- Slicing the Big Data Stack
Top 10 Webinar Downloads of 2014
- Is Revolution R Enterprise Faster than SAS? Benchmarking Results Revealed¬†– May 13, 2014
- Moving from SAS to R¬†– Aug 7, 2014
- Data Science with R¬†– Sept 25, 2014
- Applications in R – Success and Lessons Learned from the Marketplace¬†– July 22, 2014
- Introducing Revolution R Open: Enhanced, Open Source R distribution from Revolution Analytics¬†–¬†Nov 12, 2014
- Batter Up! Advanced Sports Analytics with R and Storm¬†–¬†Dec 11, 2014
- Decision Trees built in Hadoop plus more Big Data Analytics with Revolution R Enterprise¬†– Apr 14, 2014
- Building and Deploying Customer Behavior Models Webinar¬†– Feb 20, 2014
- Find the Hidden Signal in Market Data Noise¬†–¬†Mar 18, 2014
- Big Data Analytics with Teradata and Revolution Analytics¬†– November 12, 2013
Martes, 17 de febrero de 2015
A continuaci√≥n os enlazo dos “entradas de blog” muy muy interesantes:
Se trata de una introducci√≥n al la Teor√≠a de Decisi√≥n desde la perspectiva ¬†Bayesiana:
Jueves, 8 de enero de 2015
14 Reasons Why R is better than Excel
This article was first published on¬†Revolutions
The Fantasy Football Analytics blog shares these¬†14 reasons why R is better than Excel for data analysis:
- More powerful data manipulation capabilities
- Easier automation
- Faster computation
- It reads any type of data
- Easier project organization
- It supports larger data sets
- Reproducibility (important for detecting errors)
- Easier to find and fix errors
- It’s free
- It’s open source
- Advanced Statistics capabilities
- State-of-the-art graphics
- It runs on many platforms
- Anyone can contribute packages to improve its functionality
The two most important in my mind are #2 (automation) and #7 (reproducibility), reasons that apply to any GUI-driven tool. The ability to use code to repeat your analyses and reproduce the results consistently cannot be overstated.
For more detailed background behind each of these reasons, and four situations where it’s best to use Excel, check out the complete blog blost linked below.
Fantasy Football Analytics:¬†Why R is Better Than Excel for Fantasy Football (and most other) Data Analysis
S√°bado, 11 de octubre de 2014