**jared huling**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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This post is mostly an attempt to familiarize myself with Rmarkdown, jekyll, and github. I recently posted an R package (rfunctions), which contains some functions I wrote (or modified) that make my life a little easier. I’ll go through some examples in R to highlight the various functions included.

## Installation

**rfunctions** is not available on CRAN, but can be installed using the R package **devtools**. **rfunctions** can be installed with the following R code:

## Accelerated crossprod function

A project I’ve been working on requires fast evaluation of $X^TX$ for a design matrix $X$. I found a great example in the paper for RcppEigen by Douglas Bates and Dirk Eddelbuettel for just such a thing. **RcppEigen** provides a simple and effective interface between R and the blazing-fast **Eigen** C++ library for numerical linear algebra. Their example uses **inline**, a nice tool for inline C++ code in R, and I a made a proper **R** function from that. The following showcases the speed of **Eigen**. Note that since $X^TX$ is symmetric, we only have to compute half of the values, which further reduces computation time.

`crossprodcpp`

can also compute a weighted cross product $X^T W X$ where $W$ is a diagonal weight matrix

## Largest Singular Value Computation

The Lanczos algorithm is a well-known method for fast computation of extremal eigenvalues. The Golub-Kahan-Lanczos bidiagonalization algorithm is an extension of this to approximate the largest singular values of a matrix $X$ from below. The function `gklBidiag`

approximates the largest singular value of a matrix. Since GKL bidiagonalization is initialized from a random vector, we can compute a probabilistic upper bound for the singular value. The following compares the speed of `gklBidiag`

and the implementation in the popular **Fortran** library **PROPACK** found in the **svd** package

As `gklBidiag`

also works on sparse matrices (of the `SparseMatrix`

class from the **Matrix** package), I can showcase another function in **rfunctions**, `simSparseMatrix`

, which unsurprisingly simulates matrices with very few nonzero values. The nonzero values can either be all 1’s or generated from a normal distribution. The level of sparsity of the simulated matrix can be specified

## Faster Addition/Subtraction of Matrices

This may seem pointless, but I wrote functions to add and subtract matrices. It turns out my functions are faster than using the `+`

and `-`

operators. I’m sure someone will be quick to point out why using my `add()`

and `subtract()`

functions is silly and a bad idea.

The `add()`

and `subtract()`

methods for dense matrices are slower than the corresponding operators, so they’re only worth using when you have sparse matrices.

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**jared huling**.

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