I am the developer and maintainer of the package gmapsdistance for R, an interface between R and Google Maps. This is a package that uses the Google Maps Distance Matrix API to compute the distance and travel time between multiple points. If you want to perform a large number of queries you will need an API key and you need to enable the Distance Matrix API in the Google Developers Console. Google maps should be able to find both, the origin and the destination in order for the function to run. If the origin or destination contains multiple words, they should be separated by a plus sign (+). The distance is returned in meters and the time in seconds. Four different modes of transportation are allowed: "bicycling", "walking", "driving", "transit". You can find the package in CRAN following this link . Usually, the most updated version is available on github, following this link.
gmapsdistance was mentioned in R-bloggers in July of 2016. Link to the entry.
A preliminary version of gmapsdistance in Python is available in this link.
The use of graphics processing units (GPU) rather than CPU, is a powerful tool to increase the speed of your code. There have been some applications of it to Economics, in particular to perform Value Function Iteration (Check Link Here). However, I am not aware of any available code that uses such techniques for estimation strategies more suited for microeconomics. I have developed a basic code performing Maximum Likelihood estimation of a standard mincer equation. The code is available here.
David Zarruk and I are co-authors of the package Rtauchen. The package discretizes an AR(1) process following Tauchen (1986). A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.