# GPU Computing with R

Tutorials on GPU computing with R

## Hierarchical Linear Model

Linear regression probably is the most familiar technique in data analysis, but its application is often hamstrung by model assumptions. For instance, if the data has a hierarchical structure, quite often the assumptions of linear regression are feasible only at local levels. We will investigate an extension of the linear model to bi-level hierarchies.

## Bayesian Classification with Gaussian Process

Despite prowess of the support vector machine, it is not specifically designed to extract features relevant to the prediction. For example, in network intrusion detection, we need to learn relevant network statistics for the network defense. In consumer credit rating, we would like to determine relevant financial records for the credit score. As for medical genetics research, we aim to identify genes relevant to the illness.

## Significance Test for Kendall's Tau-b

A variation of the standard definition of Kendall correlation coefficient is necessary in order to deal with data samples with tied ranks. It known as the Kendall’s tau-b coefficient and is more effective in determining whether two non-parametric data samples with ties are correlated.

## Support Vector Machine with GPU, Part II

In our last tutorial on SVM training with GPU, we mentioned a necessary step to pre-scale the data with rpusvm-scale, and to reverse scaling the prediction outcome. This cumbersome procedure is now simplified with the latest RPUSVM.

## Hierarchical Cluster Analysis

With the distance matrix found in previous tutorial, we can use various techniques of cluster analysis for relationship discovery. For example, in the data set mtcars, we can run the distance matrix with hclust, and plot a dendrogram that displays a hierarchical relationship among the vehicles.

## Installing CUDA Toolkit 7.5 on Fedora 21 Linux

A discussion on how to install CUDA Toolkit in Fedora Linux.

## Installing CUDA Toolkit 7.5 on Ubuntu 14.04 Linux

A discussion on how to install CUDA Toolkit in Ubuntu Linux.

## Support Vector Machine with GPU

Most elementary statistical inference algorithms assume that the data can be modeled by linear parameters with a normally distributed error component. A new class of algorithms called support vector machine (SVM) remove such constraint.

## Kendall Rank Coefficient

The correlation coefficient is a measurement of correlation between two random variables. While its computation is straightforward, it is not readily applicable to non-parametric statistics.

## Installing GPU Packages

After installing the CUDA Toolkit and R, you can download and extract the latest rpux package in a local folder, and proceed to install rpudplus on your operating system.