"The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more)."

"The main source of this speed penalty is an insistence that the result of a matrix multiply should follow R’s rules for handling infinity, NaN (not-a-number), and NA.  These rules correspond to what happens with ordinary arithmetic operations on modern computers, which follow a standard for floating-point arithmetic in which, for example, 0/0 is NaN.  You might therefore think that nothing special is needed to arrange for matrix multiplies to produce NaNs as required.  However, R does matrix multiplications using the BLAS library, which comes in many versions, some of which may try to speed things up by avoiding “unnecessary” operations such as multiplication by zero — assuming that that will always result in zero.  However, zero times NaN or infinity is supposed to be NaN, not zero."

At some point, I’ll turn to my favourite web application combo: Sinatra + MongoDB + Highcharts, to visualize these data dynamically on a web page. For now though, we can get a quick idea and create even more Friday fun by learning how to use RApache to run and view R code in the browser.

"As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks. However, current implementations of BNs only focus on different sub-aspects of this model and do not allow for a seamless integration into existing preprocessing pipelines. RESULTS: BoolNet efficiently integrates methods for synchronous, asynchronous and probabilistic BNs. This includes reconstructing networks from time series, generating random networks, robustness analysis via perturbation, Markov chain simulations [&c]"

"This is a guide to the R programming language."