Eli Lilly and Company
5 packages on CRAN
A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://ropensci.github.io/drake/> and the online manual <https://ropenscilabs.github.io/drake-manual/>.
Evaluate a function over a data frame of expressions.
Like similar profiling tools, the 'proffer' package automatically detects sources of slowness in R code. The distinguishing feature of 'proffer' is its utilization of 'pprof', which supplies interactive visualizations that are efficient and easy to interpret. Behind the scenes, the 'profile' package converts native Rprof() data to a protocol buffer that 'pprof' understands. For the documentation of 'proffer', visit <https://r-prof.github.io/proffer>. To learn about the implementations and methodologies of 'pprof', 'profile', and protocol buffers, visit <https://github.com/google/pprof>. <https://developers.google.com/protocol-buffers>, and <https://github.com/r-prof/profile>, respectively.
Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.
This queue is a data structure that lets parallel processes send and receive messages, and it can help coordinate the work of complicated parallel tasks. Processes can push new messages to the queue, pop old messages, and obtain a log of all the messages ever pushed. File locking preserves the integrity of the data even when multiple processes access the queue simultaneously.