• study_from_xml() loads directories of xml files
  • search_full_text() to search the full text of study full_text objects.
  • included “grobid” directory in system file for XML examples
  • scivrs_app() now takes an optional study argument to load a study object and returns the edited study object
  • study_from_xml() loads a study object from a Grobid-encoded xml file.
  • fixed bug in study_power() that produced errors for long (as opposed to wide) data with within-subject factors
  • changed the returned llist from get_power() to return all analysis results, even those that aren’t used in hypothesis criteria
  • as always, more app updates
  • Lots of changes to scivrs_app()
    • It’s still not-production ready
    • But check out the demo
  • get_orcid() lets you look up ORCiDs from family and given name
  • New vignette about CRediT authorship format and ORCiDs
  • Updated for faux 0.0.1.4 (no seed argument)
  • New vignette
  • make_script(), make_data() functions
  • new capabilities for get_results() function
  • Vignette on exporting study materials from the meta-study file.
  • Better output_**** functions
  • Each study object now loads its data and functions in its own environment to avoid namaspace clashes
  • Various bug fixes to prevent crashes
  • Demo app (in progress) with scivrs_app()
  • Fixes for codebook changes in faux
  • New codebook vignette and enhanced codebook functions from faux
  • Streamlined how code is stored in the JSON file
  • added get_result function
  • More breaking changes to update the format for the preprint
  • Loading study objects from JSON works now
  • study_report deprecated and function merged to study_save
  • added get_data function
  • Added the study_power function. This function is experimental. Check power analyses with an external package before using for important decisions.
  • Added a NEWS.md file to track changes to the package.
  • Lots of breaking changes to align the format with the preprint
    Lakens, D., & DeBruine, L. M. (2020, January 27). Improving Transparency, Falsifiability, and Rigour by Making Hypothesis Tests Machine Readable. https://doi.org/10.31234/osf.io/5xcda