It seems as though every aspect of my research involves some sort of programming: experiment design (MATLAB), modeling/simulation (MATLAB), analysis (MATLAB/R), statistical computation (R), graphics (MATLAB/R/\LaTeX) and writing (\LaTeX). The only exception thus far is reading.

In the sections below, I will be releasing various scripts and functions under the GNU General Public License. Stay tuned.


I have tendency to test out ideas quickly or prototype. As a result, spending a disproportionate amount of time on the nuts and bolts wasteful. This is where MATLAB excels. Therefore, I tend to stay away from faster programming languages such as FORTRAN and C unless efficiency becomes a critical factor. In the event that I cannot get around loops (no pun intended) quick enough, I code mex (MATLAB executables) files and C/FORTRAN functions to interface with MATLAB.

Matlab code here


As indicated previously, I use R for stats and graphics, but mostly for the latter. R has a great graphics package, ggplot2, which is very customizable and is efficient for displaying high-dimensional data by using facets.

R code here


This piece of software has been around for a relatively long time. Its usage cuts down on time spent tediously formatting (done in Word or other WYSIWYG word processors) by using optimal placement of all characters on the page. It also has the ability to cross-reference and number tables and figures. But really, the way it makes equations look, is to die for. Another great feature that is naturally part of \LaTeX is the bibliographic manager and formatter \textrm{\textsc{Bib}}\TeX. Together with Papers or Mendeley (or whatever your favourite poison is), including citations and bibliographies into \LaTeX documents is essentially a zero-effort process.

\LaTeX code here

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