rOpenSci | rOpenSci Statistical Software Peer Review

rOpenSci Statistical Software Peer Review

rOpenSci is currently working to expand our peer review system beyond its current scope to explicitly review packages that implement statistical algorithms. This requires an expansion of our editor and reviewer community to bring in new expertise, and new sets of standards and approaches to deal with the particular challenges of statistical methods. We aim to initially include packages from the following 11 categories:

  1. Bayesian and Monte Carlo Routines
  2. Dimensionality Reduction, Clustering, and Unsupervised Learning
  3. Machine Learning
  4. Regression and Supervised Learning
  5. Probability Distributions
  6. Wrapper Packages
  7. Networks
  8. Exploratory Data Analysis (EDA) and Summary Statistics
  9. Workflow Support
  10. Spatial Analyses
  11. Time Series Analyses

Our process to develop these standards is primarily documented and organised through our online “living document”. We anticipate the system being ready for initial submissions early in 2021. Until then, we welcome any enquiries from anyone interested in engaging with the system, including developers who might be interested in pre-submission enquiries. Please contribute via our discussion forum, or contact Mark Padgham ([email protected]).

This work is supported through a grant from the Alfred P. Sloan Foundation.

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Our Advisory Board

Our advisory board members are volunteers.

Ben Bolker
McMaster University
Max Kuhn
RStudio
Rebecca Killick
Lancaster University
Leonardo Collado-Torres
Lieber Institute for Brain Development
Stephanie Hicks
Johns Hopkins University
Paula Moraga
King Abdullah University of Science and Technology

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