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targets:Dynamic Function-Oriented 'Make'-Like Declarative Pipelines
Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).
Maintained by William Michael Landau. Last updated 1 days ago.
data-sciencehigh-performance-computingmakepeer-reviewedpipeliner-targetopiareproducibilityreproducible-researchtargetsworkflow
978 stars 15.16 score 4.6k scripts 22 dependentsropensci
tarchetypes:Archetypes for Targets
Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) <doi:10.21105/joss.00550>.
Maintained by William Michael Landau. Last updated 1 days ago.
data-sciencehigh-performance-computingpeer-reviewedpipeliner-targetopiareproducibilitytargetsworkflow
142 stars 11.27 score 1.7k scripts 10 dependentsropensci
jagstargets:Targets for JAGS Pipelines
Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS' (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.
Maintained by William Michael Landau. Last updated 4 months ago.
bayesianhigh-performance-computingjagsmaker-targetopiareproducibilityrjagsstatisticstargetscpp
10 stars 6.95 score 32 scriptsropensci
stantargets:Targets for Stan Workflows
Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'stantargets' R package leverages 'targets' and 'cmdstanr' to ease these burdens. 'stantargets' makes it super easy to set up scalable Stan pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. 'stantargets' can access all of 'cmdstanr''s major algorithms (MCMC, variational Bayes, and optimization) and it supports both single-fit workflows and multi-rep simulation studies. For the statistical methodology, please refer to 'Stan' documentation (Stan Development Team 2020) <https://mc-stan.org/>.
Maintained by William Michael Landau. Last updated 2 months ago.
bayesianhigh-performance-computingmaker-targetopiareproducibilitystanstatisticstargets
49 stars 6.85 score 180 scriptsropensci
geotargets:'Targets' Extensions for Geographic Spatial Formats
Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions.
Maintained by Nicholas Tierney. Last updated 9 days ago.
geospatialpipeliner-targetopiarasterreproducibilityreproducible-researchtargetsvectorworkflow
73 stars 6.79 score