Posts
subscribe via RSS

Multiple Linear Regression Proofs
Intro Below are a few proofs regarding the least square derivation associated with multiple linear regression (MLR). These proofs are useful for understanding where MLR algorithm originates from. In particular, if one aims to write their own implementation, these proofs provide a means to understand: What logic is being used? How does the logic apply in a procedural form? Why is this logic present? Multiple Linear Regression (MLR) Definition Formula Responses: Errors: Design Matrix: Parameters:...
Read more... 
Downloading and Installing RStudio Desktop
The contents of this tutorial is geared toward downloading and installing the RStudio Desktop IDE on: Windows macOS Linux Ubuntu / Debian RedHat / openSUSE / Fedora Click one of the above links to be taken to the appropriate installation section. What is RStudio Desktop IDE? Before downloading or installing any software, it may help to have a bit of a background as to what you are obtaining. Thus, let’s briefly look at “What is...
Read more... 
Downloading and Installing R
The contents of this tutorial is geared toward downloading and installing R the statistical programming language on: Windows macOS Linux Ubuntu Debian Redhat / CentOS SUSE Click one of the above links to be taken to the appropriate installation section. What is R? Before downloading or installing any software, it may help to have a bit of a background as to what you are obtaining. Thus, let’s briefly look at “What is R?” R is:...
Read more... 
Downloading and Installing git
The contents of this tutorial is geared toward downloading and installing git the version control system on: Windows macOS Linux Ubuntu / Debian RedHat / CentOS / Fedora SUSE Click one of the above links to be taken to the appropriate installation section. Afterwards, please make sure to look at the configure git section of the tutorial. Download and Installation of git Before downloading or installing any software, it may help to have a bit...
Read more... 
Making a Uniform Pseudo Random Number Generator (PRNG) with `sitmo`
Editor’s Note: This post is published within the sitmo R package as a vignette. Intro Many of the random number generators for various distributions rely on the Probability Integral Transformation theorem. Succintly stated as: Theorem Let $X$ be a random variable that has a cumulative distribution function (CDF) of $F_X\left({x}\right)$. Then, define random variable $U = F_X\left({X}\right)$. Thus, $U$ is a uniform distribution. Proof Given any random variable X, define $U = F_X\left({X}\right)$. Then: Therefore,...
Read more... 
sitmo v1.1.0 Released
Moving along with the package updates, we come across the sitmo headeronly R package that falls victim to R 3.4’s newest CRAN check: Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’. Packages that encounter this note need to simply register the compiled code routines using the procedure described in this post. The objective of sitmo is to provide the means for performing parallel draws from a Parallel Pseudo Random Number Generator (PPRNG). One of the nice features...
Read more... 
Registration of Entry Points in Compiled Code Loaded into R
Intro With the arrival of R 3.4, a new era of CRAN checks regarding C++ registration was ushered in. In particular, CRAN checks now issue the following “NOTE”: checking compiled code … NOTE File ‘packagename/libs/packagename.so’: Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’ The motivation behind the addition of this check can be found in Writing R Extensions: Section 5.4 Registering native routines and on Rdevel: [Rd] Registration of native routines. More aptly, the motivation can be...
Read more... 
msos v1.1.0 Released
With R 3.4 landing, it’s time to revisit msos  Multivariate Statistics: Old School  or the second ever R package I ever created. The origins of this package are related to taking, circa 2013, Professor Emeritus John Marden excellent STAT 571: Multivariate Analysis course at the University of Illinois at UrbanaChampaign (UIUC) and not being able to instanteously load in a few of the datasets – mainly the famed “Spam” data set – from...
Read more... 
visualize v4.3.0 Released
Courtesy of R 3.4 being released last Friday, I’ve been on a package updating spree. The first package to receive such treatment is actual the first R package I ever wrote: visualize. Back in the day, before RStudio really was prevalent and devtools was as feature rich as it is today, I learned how to build packages with the good ol’ Terminal using R CMD build and R CMD check. I ended up writing up...
Read more... 
A modulefile Approach to Compiling R on a Cluster
This is a follow up post to Working with R on a Cluster. Previously, we discussed ways to work with R in a purely distributed command line interface (CLI) environment. Within this post, we’ll detail how to setup your own private installation of R on a cluster that supports modulefiles. Motivation Lately, I’ve been needing to use more cutting edge versions of R than what has been made available by the campus cluster staff. The...
Read more...