Microsoft R Open 3.4.3 now available

(This article was first published on Revolutions, and kindly contributed to R-bloggers) Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.4.3 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to the latest R (version 3.4.3) and updates the bundled packages (specifically:…

via Microsoft R Open 3.4.3 now available — R-bloggers

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RStudio Connect v1.5.1

(This article was first published on RStudio Blog, and kindly contributed to R-bloggers) We’re pleased to announce RStudio Connect 1.5.12. This release includes support for viewing historical content, per-application timeout settings, and important improvements and bug fixes. Historical Content RStudio Connect now retains and displays historical content. By selecting the content’s history, viewers can easily…

via RStudio Connect v1.5.12 — R-bloggers

R 3.4.3 is released (a bug-fix release)

(This article was first published on R – R-statistics blog, and kindly contributed to R-bloggers) R 3.4.3 (codename “Kite-Eating Tree”) was released last week. You can get the latest binaries version from here. (or the .tar.gz source code from here). As mentioned by David Smith, R 3.4.3 is primarily a bug-fix release: It fixes an issue with incorrect time zones…

via R 3.4.3 is released (a bug-fix release) — R-bloggers

Regresión logística y datos con grupos linealmente separables

Recientemente, los participantes en una encuesta realizada por la página web kaggle afirmaban que el modelo de regresión logística era la técnica estadística más utilizada en ciencia de datos. Este modelo proporciona una regla lineal de clasificación. Sin embargo, si tratamos de ajustar un modelo de regresión logística a datos con dos grupos linealmente separables (en principio, […]

via Regresión logística y datos con grupos linealmente separables — Caminos aleatorios

Practical Machine Learning with R and Python – Part 6

(This article was first published on R – Giga thoughts …, and kindly contributed to R-bloggers) Introduction This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. In this series I included the implementations of the most common Machine Learning algorithms in R and Python. The algorithms…

via Practical Machine Learning with R and Python – Part 6 — R-bloggers

Ensemble learning for time series forecasting in R

(This article was first published on Peter Laurinec, and kindly contributed to R-bloggers) Ensemble learning methods are widely used nowadays for its predictive performance improvement. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome accuracy of simple prediction and to avoid possible overfit. In the domain of time series forecasting, we…

via Ensemble learning for time series forecasting in R — R-bloggers