R

Continuous Integration (CI) is not a tool but a practice of continually merging in new behaviour/features into a released product. To facilitate this practice without exposing end users to unstable behaviour and bugs, testing needs to be standardised and automated. It’s no wonder then that CI is often associated with Test Driven Development (TDD), which mandates that you write your tests first, working backwards to the write the minimal code that should pass each test.

It’s taking a long time to run my genetic algorithm optimisation models recently. So much so that I’ve been looking at offloading processes to other computers lying idle on the network. The armr project aims to do this with parallel processing and Rstudio server docker images running on the raspberry pi but this is a work in progress currently, chiefly due to having to build Rstudio server from source.

Latin hypercube sampling aims to bring the best of both worlds: the unbiased random sampling of monte carlo simulation; and the even coverage of a grid search over the decision space. It does this by ensuring values for all variables are as uncorrelated and widely varying as possible (over the range of permitted values).

Docker containers for R on 32 bit ARM architectures, including Raspberry Pi