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ropensci
drake:A Pipeline Toolkit for Reproducible Computation at Scale
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://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.
Maintained by William Michael Landau. Last updated 4 months ago.
data-sciencedrakehigh-performance-computingmakefilepeer-reviewedpipelinereproducibilityreproducible-researchropensciworkflow
1.3k stars 11.49 score 1.7k scripts 1 dependents
cran
ocd:High-Dimensional Multiscale Online Changepoint Detection
Implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.
Maintained by Yudong Chen. Last updated 4 years ago.
1.70 score