Build R-3.0.1 with Intel® C++ Compiler and Intel® MKL on Linux*

R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. This guide will show how to build with the Intel® C++ Compiler and BLAS and LAPACK libraries within Intel® Math Kernel Library (Intel® MKL) to improve the performance of R runtime framework.
  • Linux*
  • C/C++
  • R
  • Intel® C++ Compiler
  • Intel® Math Kernel Library
  • statistics
  • r with icc
  • Optimización
  • Using Intel® MKL with R


    R is a programming language for statistical computing. The open source package also provides an environment for creating and running R programs. This guide will show how to use the BLAS and LAPACK libraries within Intel® Math Kernel Library (Intel® MKL) to improve the performance of R. To use other Intel MKL functions, you read this article on Extending R with Intel MKL.


  • Desarrolladores
  • R
  • Intermedio
  • Intel® Math Kernel Library
  • r with mkl
  • statistics
  • Herramientas de desarrollo
  • Optimización
  • Computación en paralelo
  • Intel® Summary Statistics Library: how to detect outliers in datasets?

    Earlier I computed various statistical estimates like mean or variance-covariance matrix using Intel® Summary Statistics Library. In those cases I knew for sure that my datasets did not contain “bad” observations (points which do not belong to the distribution which I observed) or outliers. However, in some cases we need to deal with datasets which are contaminated with outliers.

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