Reproducible Pipeline Infrastructure for Neuroscience R package ravepipeline version 0.1.0 Defines the underlying pipeline structure for reproducible neuroscience, adopted by 'RAVE' (reproducible analysis and visualization of intracranial electroencephalography); provides high-level class definition to build, compile, set, execute, and share analysis pipelines. Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard templates for extending and building customized pipelines. See the full documentations at <https://rave.wiki>; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>), or run citation("ravepipeline") for details. Software Imports: stats, tools, utils, base64enc, callr, cli, digest, fastmap, future, fst (>= 0.9.8), glue, jsonlite, knitr, promises, R6, remotes, rlang, targets, uuid, yaml, logger Suggests: ellmer (>= 0.4.0), dipsaus, filearray, future.apply, globals, ieegio, pkgsearch, rpymat, rmarkdown, rstudioapi, shidashi, threeBrain, testthat (>= 3.0.0), visNetwork, later, shiny, mirai Zhengjia Wang <dipterix.wang@gmail.com> Comprehensive R Archive Network (CRAN) Zhengjia Wang [aut, cre, cph], John Magnotti [ctb, res], Xiang Zhang [ctb, res], Michael Beauchamp [ctb, res], Trustees of University of Pennsylvania [cph] (Copyright Holder) MIT + file LICENSE (https://CRAN.R-project.org/package=ravepipeline/LICENSE) 2026-05-30 application/tgz https://CRAN.R-project.org/package=ravepipeline doi:10.32614/CRAN.package.ravepipeline en-US