Intel® Summary Statistics Library: how to deal with missing observations?

Authored by Dmitry Kabaev (Intel)

Real life datasets can have missing values.

Last updated on 06/15/2012 - 14:37

Intel® Summary Statistics Library: how to use the robust methods?

Authored by Dmitry Kabaev (Intel)

Intel® Summary Statistics Library provides several opportunities for processing the datasets “contaminated” with

Last updated on 06/15/2012 - 14:37

Intel® Summary Statistics Library: how fast is the algorithm for detection of outliers?

Authored by Dmitry Kabaev (Intel)

In one of my previous posts I described the

Last updated on 06/15/2012 - 14:37

Intel® Summary Statistics Library: why not to use multi-core advantages?

Authored by Dmitry Kabaev (Intel)

In my previous posts I described some features and usage model of Intel® Statistics Library.

Last updated on 06/15/2012 - 14:37

Intel® Summary Statistics Library: how to detect outliers in datasets?

Authored by Dmitry Kabaev (Intel)

Earlier I computed various statistical estimates like mean or variance-covariance matrix using Intel® Summary Statistics Lib

Last updated on 08/02/2013 - 15:28

Intel® Summary Statistics Library: how to process data in chunks?

Authored by Dmitry Kabaev (Intel)
In my previous post I considered computation of statistical estimates for in-memor Last updated on 06/15/2012 - 14:37

Intel® Summary Statistics Library: Several Estimates at One Stroke

Authored by Dmitry Kabaev (Intel)

Today it was necessary for me to compute statistical estimates for a dataset. The observations are weighted, and only several components of the random vector had to be analyzed.

Last updated on 06/15/2012 - 14:37

Extending R with Intel MKL

Authored by TODD R. (Intel)

Introduction

Last updated on 04/26/2013 - 15:48

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

Authored by QIAOMIN Q. (Intel)
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... Last updated on 07/02/2013 - 21:00

Using Intel® MKL with R

Authored by TODD R. (Intel)

Overview

Last updated on 12/30/2014 - 22:23