Appendix E — Function reference
This appendix lists every exported function in Metacheck, generated directly from the package’s roxygen documentation (the same source ?function_name reads from). It is grouped by task rather than alphabetically, so functions used together stay together. Unlike a hand-written summary, this page is regenerated from the package’s documentation, so it matches the package’s exports as of the last time it was regenerated, from metacheck’s development version.
As of this build, that is 287 functions across 20 groups.
E.1 Getting a paper to work with
E.1.1 demopaper
Get demo paper
demopaper(paper = c("to_err_is_human", "stroop_effect"))"to_err_is_human" (the default) is a short paper written to show metacheck’s checks. "stroop_effect" is a small reproducible analysis whose data and code are bundled as a demo repository (see demofile("repo")); the paper is its manuscript, converted with Grobid.
Returns: paper object
E.1.2 test_paper
Test paper
test_paper(text = LETTERS, url = character(0))Create a paper object with the specified text (mainly for testing/demos).
Returns: a paper object
E.1.3 demofile
Get a demo file
demofile(ext = c("json", "pdf", "docx", "doc", "xml", "qmd", "repo"))Return the file path for various versions of the demo paper. Use demopaper() to directly read it as a paper object from the json file.
Returns: file path (a folder for "repo")
E.1.4 read
Read in grobid XML or bibr JSON
read(file_path, include_images = FALSE, recursive = FALSE)Returns: a paper or paperlist
E.1.5 paper
Create a paper object
paper(id = NULL, ...)Create a new paper object or load a paper from PDF or XML
Returns: An object with class scivrs_paper
E.1.6 paperlist
Create a paperlist object
paperlist(..., merge_duplicates = FALSE)Create a new paperlist object from individual paper objects or lists of paper objects
Returns: An object with class scivrs_paperlist
E.1.7 paper_id
Get Paper IDs
paper_id(paper)Returns: a vector of paper_ids
E.1.8 paper_table
Paper tables
paper_table(paper, table, cols = NULL)Return a table from a paper object or concatenate tables across a list of paper objects.
Returns: a merged table
E.1.9 ref_table
Reference and DOI table
ref_table(paper)Return a table with fixed DOIs and reference text from a paper object or concatenate tables across a list of paper objects.
Returns: a merged table
E.1.10 paper_validate
Validate a Paper Object
paper_validate(paper)Checks if a paper object conforms to the JSON schema.
Returns: TRUE or error
E.1.11 paper_write
Write paper
paper_write(paper, file_name = NULL, save_path, schema_version = NULL)Save a paper as a JSON file.
Returns: the path to the JSON file
E.1.12 fig_image_view
View a figure image
fig_image_view(paper, figure_id = 1)Returns: plots the figure
E.2 Managing collections of papers
E.2.1 papers_load
Load a paper corpus
papers_load(
name,
repo = "scienceverse/papers",
cache = FALSE,
overwrite = FALSE
)Downloads a paper corpus RDS file from the scienceverse/papers GitHub repository and loads it into R as a paperlist object. By default the file is downloaded to a temporary location and discarded after loading, which is the right choice for one-off use. Set cache = TRUE to save it permanently in the user data directory instead, so subsequent calls reuse the cached copy rather than re-downloading. Use papers_remove() to delete a cached corpus.
Returns: a paperlist object
E.2.2 papers_available
List available paper corpora
papers_available(repo = "scienceverse/papers")Queries the GitHub Releases API for the scienceverse/papers repository and returns a table of available paper corpora that can be loaded with papers_load().
Returns: a data frame with columns name, tag, size_mb, and cached
E.2.3 papers_metadata
Get a paper corpus’s Dublin Core metadata
papers_metadata(name, repo = "scienceverse/papers")Fetches and parses metadata.json for a corpus from the scienceverse/papers GitHub repository. This file documents provenance that is not stored in the corpus .rds itself: how the corpus was built, what years/license it covers, and so on. See each corpus’s README.md on the papers repository for a fuller narrative description, including known gaps and data-quality caveats.
Returns: a named list of the corpus’s Dublin Core metadata fields (colons in the original dc:field names are replaced with underscores, e.g. dc:title becomes dc_title)
E.2.4 papers_remove
Remove a cached paper corpus
papers_remove(name)Deletes the locally cached RDS file for a corpus to free disk space.
Returns: TRUE invisibly if deleted, FALSE if not cached
E.3 Importing papers from PDFs and XML
E.3.1 convert
Convert documents
convert(
file_path,
save_path,
method = c("auto", "bibr", "grobid", "xml"),
crossref_lookup = FALSE,
keep_xml = TRUE,
...
)Uses grobid or bibr to convert a file to paper format.
Returns: the path to the JSON file
E.3.2 convert_grobid
Convert a PDF to Grobid XML
convert_grobid(
file_path,
save_path,
api_url = "https://grobid.hti.ieis.tue.nl",
start_page = -1,
end_page = -1,
consolidate_citations = 0,
consolidate_header = 0,
consolidate_funders = 0
)This function uses a GDPR-compliant public grobid server maintained by Eindhoven Technical University. You can set up your own local grobid server following instructions from https://grobid.readthedocs.io/ and set the argument api_url to its path (probably http://localhost:8070). See https://github.com/grobidOrg/grobid#demo for other publicly available servers (we cannot guarantee their privay).
Returns: XML object
E.3.3 convert_bibr
Convert documents using bibr
convert_bibr(
file_path,
save_path,
backend = c("auto", "surf", "scivrs", "selfhosted"),
api_key = NULL,
api_url = NULL,
include_figures = FALSE,
start_page = 1,
end_page = Inf,
poll_interval = 2,
timeout = 600
)Converts document files (PDF, DOC, DOCX) to structured JSON using the bibr extraction service. Supports three backends: metacheck’s API on SURF Research Cloud ("surf"), which converts PDF files and needs no API key; the Scienceverse platform ("scivrs") which uses a job queue with load balancing; and a self-hosted bibr instance ("selfhosted") for direct API access.
Returns: Path(s) to the saved JSON file(s)
E.3.4 grobid_to_bibr
Convert Grobid TEI XML file to bibr format
grobid_to_bibr(
xml_path,
save_path,
crossref_lookup = FALSE,
schema_version = NULL
)Returns: a paper object
E.4 Searching and extracting from the text
E.4.1 search_text
Search text
text_search(
paper,
pattern = ".*",
return = c("sentence", "paragraph", "section", "header", "match", "paper_id"),
ignore.case = TRUE,
fixed = FALSE,
perl = FALSE,
exclude = FALSE,
search_header = FALSE,
include_refs = FALSE
)
search_text(
paper,
pattern = ".*",
return = c("sentence", "paragraph", "section", "header", "match", "paper_id"),
ignore.case = TRUE,
fixed = FALSE,
perl = FALSE,
exclude = FALSE,
search_header = FALSE,
include_refs = FALSE
)Search the text of a paper or list of paper objects. Also works on the table results of a text_search() call.
Returns: a data frame of matches
E.4.2 text_search
Search text
text_search(
paper,
pattern = ".*",
return = c("sentence", "paragraph", "section", "header", "match", "paper_id"),
ignore.case = TRUE,
fixed = FALSE,
perl = FALSE,
exclude = FALSE,
search_header = FALSE,
include_refs = FALSE
)
search_text(
paper,
pattern = ".*",
return = c("sentence", "paragraph", "section", "header", "match", "paper_id"),
ignore.case = TRUE,
fixed = FALSE,
perl = FALSE,
exclude = FALSE,
search_header = FALSE,
include_refs = FALSE
)Search the text of a paper or list of paper objects. Also works on the table results of a text_search() call.
Returns: a data frame of matches
E.4.3 expand_text
Expand text
text_expand(
results_table,
paper,
expand_to = c("sentence", "paragraph", "div", "section"),
plus = 0,
minus = 0
)
expand_text(
results_table,
paper,
expand_to = c("sentence", "paragraph", "div", "section"),
plus = 0,
minus = 0
)If you have a table resulting from text_search() or a module return object, you can expand the text column to the full sentence, paragraph, or section. You can also set plus and minus to append and prepend sentences to the result (only when expand_to is “sentence”).
Returns: a results table with the expanded text
E.4.4 text_expand
Expand text
text_expand(
results_table,
paper,
expand_to = c("sentence", "paragraph", "div", "section"),
plus = 0,
minus = 0
)
expand_text(
results_table,
paper,
expand_to = c("sentence", "paragraph", "div", "section"),
plus = 0,
minus = 0
)If you have a table resulting from text_search() or a module return object, you can expand the text column to the full sentence, paragraph, or section. You can also set plus and minus to append and prepend sentences to the result (only when expand_to is “sentence”).
Returns: a results table with the expanded text
E.4.5 text_peek
Peek at the first lines of a text file
text_peek(path, n = 20L)Reads the first n lines of a file as text, tolerating the encodings research data actually ships in: a UTF-8/UTF-16 BOM, UTF-16 (E-Prime exports), and Latin-1 bytes that would otherwise make readLines() output error in later string operations. Intended for cheap format sniffing – deciding what a file is before committing to a reader – not for reading data.
Returns: a character vector of at most n lines (UTF-8), or character(0).
E.4.6 extract_eq
Extract Equations
extract_eq(paper)List all equations in the text, returning the matched text (e.g., ‘t(28) = 2.4’, ‘p = 0.04’) and document location in a table. This is the canonical extractor for reported statistics and effect sizes; modules that need statistics should read from this table rather than re-scanning the text.
Returns: a data frame with one row per equation and the columns lhs (the statistic name, e.g. “t”, “F”, “p”), df (parenthetical degrees of freedom such as “(28)” or “(2, 57)”, otherwise NA), comp (the comparator, e.g. “=”), rhs (the reported value as text), grp_id (groups equations in the same sentence), text_id, and paper_id.
E.4.7 extract_p_values
Extract P-Values
extract_p_values(paper)List all p-values in the text, returning the matched text (e.g., ‘p = 0.04’) and document location in a table.
Returns: a table
E.4.8 extract_urls
Extract URLs
extract_urls(paper)Get a table of URLs from a paper or paperlist. Matches urls that start with http or doi:
Returns: a table
E.4.9 extract_tests
Extract the statistical tests a paper reports
extract_tests(paper)Builds the paper-side counterpart to the analysis output that reproducibility_check extracts: one row per reported TEST, with its components kept together and the sentence it was reported in retained, so a matched result can be traced back to the claim it supports.
Returns: a data.frame, one row per reported test, with paper_id, test_no (sequential within the paper), text_id, paragraph_id, section_id, sentence (the reporting sentence), anchor (the test statistic the report is built around, e.g. "t"), n_components, components (a list-column of name/comp/value/df), and reported (the test rendered back as text, e.g. "t(23) = 3.77, p = .001, d = 0.77").
E.4.10 stats
Check Stats
stats(text, ...)Returns: a table of statistics
E.4.11 json_expand
Expand a JSON column
json_expand(table, col = "answer", suffix = c("", ".json"))It is useful to ask an LLM to return data in JSON structured format, but can be frustrating to extract the data, especially where the LLM makes syntax mistakes. This function tries to expand a column with a JSON-formatted response into columns and deals with it gracefully (sets an ‘error’ column to “parsing error”) if there are errors. It also fixes column data types, if possible.
Returns: the table plus the expanded columns
E.4.12 causal_relations
Extract causal relations from sentence(s) via a Hugging Face Space
causal_relations(
sentence,
rel_mode = "auto",
rel_threshold = 0.5,
cause_decision = "cls+span",
timeout = 10,
verbose = FALSE
)Sends one or more input sentences to a public Gradio app hosted on Hugging Face (the lakens-causal-sentences Space), created based on code by Rasoul Norouzi, retrieves the result via Server-Sent Events (SSE), and returns a tidy data frame with one row per detected cause–effect relation.
Returns: A base data.frame with columns:
sentence(character): the original input sentence,causal(logical): whether the sentence is causal per the model,cause(character): extracted cause span (orNA),effect(character): extracted effect span (orNA).
E.5 Running checks (modules)
E.5.1 module_list
List modules
module_list(
module_dir = system.file("modules", package = "metacheck"),
external = TRUE
)Returns: a data frame of modules; source is the GitHub repository of an installed module, or NA
E.5.2 module_run
Run a module
module_run(paper, module, ...)Returns: a list of the returned table and report text
E.5.3 module_help
Get Module Help
module_help(module = NULL)See the help files for a module by name (get a list of names from module_list())
Returns: the help text
E.5.4 module_info
Get module information
module_info(module)Returns: a list of module info
E.5.5 module_template
Create a Module from a Template
module_template(module_name, path)Returns: the file path (invisibly)
E.5.6 get_prev_outputs
Get Previous Outputs
get_prev_outputs(module, item, parent_n = 2)A helper for creating modules. Checks for previous module outputs in a chain and returns the named list item if it exists in any parent environment.
Returns: the extracted list item, or NULL if not found
E.5.7 module_install
Install modules from GitHub
module_install(repo, ref = NULL, overwrite = FALSE)Downloads a public GitHub repository that holds one or more metacheck modules and saves it in the metacheck user data directory, so its modules can be used by name in module_run(), report(), module_list() and module_help(), like the built-in modules.
Returns: a data frame of the installed repository (see module_installed()), invisibly
E.5.8 module_update
Update installed modules
module_update(repo = NULL)Downloads the latest commit of the branch or tag each repository was installed from (see module_install()). A repository that is already up to date is not downloaded again.
Returns: a data frame of the installed repositories (see module_installed()), invisibly
E.5.9 module_remove
Remove installed modules
module_remove(repo)Deletes a repository of modules installed with module_install().
Returns: TRUE invisibly if removed, FALSE if not installed
E.5.10 module_installed
List installed module repositories
module_installed()Lists the GitHub repositories installed with module_install(). Use module_list() to see their modules together with the built-in ones.
Returns: a data frame with one row per repository: repo, the ref (branch, tag or commit) it was installed from, the sha of the installed commit, when it was installed, its modules, and the local path
E.6 Building a report
E.6.1 report
Create a Report
report(
paper,
modules = c("prereg_check", "funding_check", "coi_check", "power", "repo_check",
"code_check", "stat_check", "stat_p_exact", "stat_p_nonsig", "stat_effect_size",
"marginal", "ref_replication", "ref_retraction", "ref_pubpeer", "ref_summary"),
output_file = file.path(tempdir(), paste0(paper$paper_id, "_report.", output_format)),
output_format = c("html", "qmd"),
report_type = c("full", "brief", "simple", "simple_brief", "checklist"),
args = list()
)Run specified modules on a paper and generate a report in quarto (qmd), html, or pdf format.
Returns: the module output, invisibly, with the report’s file path in its save_path attribute (a vector of paths, one per paper, when paper is a paperlist)
E.6.2 report_module_run
Run modules for a report
report_module_run(paper, modules, args = list())Runs modules in order on the paper and orders by section and traffic light.
Returns: a list of module outputs
E.6.3 module_report
Report from module output
module_report(module_output, header = 3)Returns: text
E.6.4 report_qmd
Create Report from Module Output
report_qmd(module_output, paper = list())Returns: report text
E.6.5 report_app
Launch Report App
report_app(quiet = FALSE, ...)Launch the Report app: upload a PDF and generate a report with one click, with privacy options for what is sent to external servers.
Returns: NULL (invisibly)
E.6.6 report_repository
Create a Report for a Local Repository
report_repository(
path,
output_file = NULL,
output_format = c("html", "qmd"),
report_type = c("full", "brief", "simple", "simple_brief", "checklist"),
modules = c("repo_check", "code_check", "data_check", "codebook_check"),
args = list()
)Runs the repository modules on a folder of files on your own computer and writes a single report. Use it on a repository you have downloaded (for example with osf_file_download()) to see what was shared and what could be improved, before archiving the files somewhere permanent.
Returns: the module output, invisibly, with the report’s file path in its save_path attribute
E.6.7 report_type
Set or get the report type
report_type(report_type = NULL)Controls two independent things about how report()/report_repository() build a report:
rendering mechanics – a full, interactive HTML report (light/dark theme toggle, sortable/paginated JavaScript tables, tabbed sections) vs. a “simple” static report with none of that – plain tables, stacked headings instead of tabs, a single theme, no inline
<script>content. Email clients and mail scanners routinely flag the embedded JavaScript a full report carries (even though it is inert without a browser), so a “simple” report type is the setting to use before emailing a report.content amount – every module’s full detail vs. only the modules flagged red/yellow/fail (i.e. what needs attention), with just their one-line summary and no expandable detail, “How It Works” callout, or validation note. This is the “brief” setting, for getting to “what to improve” as fast as possible.
Returns: the current option value
E.6.8 FAIR_repo
Convert a research folder into a clean, FAIR copy
FAIR_repo(path, output_dir, paper = NULL, overwrite = FALSE, execute = FALSE)Writes a reorganised copy of a folder of research files (data, code, materials, documentation) on your own computer. Nothing is downloaded and no link is followed: only the folder you name is read, and it is not changed. The folder is first copied to a temporary folder, and the checks run on that copy, so nothing they write (such as code recovered from an output file) can reach your folder; the copy is removed at the end. The checks run the way report_repository() runs them: repo_check takes an inventory of the folder, and code_check, data_check, codebook_check and reproducibility_check (with execute = FALSE, so no code is run, unless you set execute) build on it. Whether a language model is used is decided by llm_use().
Returns: (invisibly) a list with output_dir, n_files_copied, n_studies, plan (the file plan, with a written column: the path each file was written to, NA when it was not), scripts (one row per changed script: target_path, paths_changed, setwd_removed, setwd_paths, libraries_added), skipped (files that could not be read), copy_failed and renamed (a file whose target another file already took), empty_plan (TRUE when the folder had no files), and the paths of the metadata written: descriptions, rocrate, requirements, logs and readme_changes. When an existing output_dir was kept, the list has only output_dir, the counts, and existed = TRUE.
E.7 Report-building helpers
E.7.1 scroll_table
Make Scroll Table
scroll_table(
table,
colwidths = "auto",
maxrows = 2,
escape = FALSE,
column = "body"
)A helper function for making module reports.
Returns: the markdown R chunk to create this table
E.7.2 report_table
Display a Table in a Report
report_table(table, colwidths = "auto", maxrows = 2, escape = FALSE)A function to display tables in reports.
Returns: the datatable (full report) or a knitr::kable object (simple report)
E.7.3 collapse_section
Make Collapsible Section
collapse_section(
text,
title = "Learn More",
callout = c("tip", "note", "warning", "important", "caution"),
collapse = TRUE
)A helper function for making module reports.
Returns: text
E.7.4 link
Make an html link
link(url, text = url, new_window = TRUE, type = "")Returns: string
E.7.5 plural
Pluralise
plural(n, singular = "", plural = "s")Helper function for conditional plurals. For example, if you want to return “1 error” or “2 errors”, you can use this in a sprintf().
Returns: a string
E.7.6 format_ref
Format Reference
format_ref(bib)Format a reference for display in a report.
Returns: formatted text
E.8 Looking up DOIs and bibliographic metadata
E.8.1 doi_clean
Clean DOIs
doi_clean(doi)Returns: a character vector of cleaned DOIs (no https://doi.org or DOI:)
E.8.2 doi_valid_format
Validate DOI format
doi_valid_format(doi)Returns: a logical vector
E.8.3 doi_resolves
Check whether a DOI resolves
doi_resolves(doi, timeout = 10)Checks the doi.org API to see if a DOI is registered and has an associated URL (using https://doi.org/api/handles). Returns TRUE if it does, FALSE if the DOI does not exist or does not have an associated URL, and NA if the test failed. Clearly invalid DOIs (i.e. not starting with “10.”) will return FALSE without server requests.
Returns: Logical vector. For each input DOI, returns TRUE if the DOI resolves, FALSE if it does not resolve (or does not start with 10.), and NA if the check failed.
E.8.4 doi_lookup
Doi.org Info from DOI
doi_lookup(doi)Returns: data frame with DOIs and info
E.8.5 crossref_doi
CrossRef Info from DOI
crossref_doi(
doi,
select = c("DOI", "type", "title", "author", "container-title", "volume", "issue",
"page", "URL", "abstract", "year", "error")
)Valid selects for crossref API are:
Returns: data frame with DOIs and info
E.8.6 crossref_query
Look up Reference in CrossRef
crossref_query(
ref,
min_score = 50,
rows = 1,
select = c("DOI", "score", "type", "title", "author", "editor", "publisher",
"container-title", "year", "volume", "issue", "page", "URL")
)Returns: doi
E.8.7 openalex_doi
OpenAlex info from DOI
openalex_doi(doi, select = NULL)See details for a list of root-level fields that can be selected.
Returns: list with DOIs and info
E.8.8 openalex_query
Look up a reference in OpenAlex
openalex_query(title, source = NA, authors = NA, strict = TRUE)Returns: A data frame with citation info
E.8.9 openalex_key
Set or get an OpenAlex API key
openalex_key(key = NULL)OpenAlex works without a key, with a budget of US$0.10 per day: about 1,000 searches, while looking up a work by its DOI or OpenAlex ID is free. A free key raises the budget to US$1 per day. Make one by signing up at https://openalex.org/signup and copying the key from https://openalex.org/settings/api-key.
Returns: the current key (character; "" when none is set)
E.8.10 datacite_doi
Doi.org Info from DataCite
datacite_doi(doi)Returns: bib_match data frame
E.8.11 add_bib_match
Match table from bib table
add_bib_match(paper, min_score = 50)Returns: the paper or paperlist with bib_match table added
E.9 Databases of comments, replications, retractions
E.9.1 pubpeer_comments
Get Pubpeer Comments
pubpeer_comments(doi)Takes a DOI, and retrieves information from pubpeer related to post-publication peer review comments.
Returns: a dataframe with information from pubpeer
E.9.2 FLoRA
FORRT Replication Database (FLoRA)
FLoRA()FLoRA database containing DOIs of original studies and replications. Use FLoRA_date() to find the date it was downloaded, and FLoRA_update() to update it.
Returns: a data frame
E.9.3 FLoRA_update
Update FLoRA
FLoRA_update()metacheck comes with a built-in data frame called FLoRA. We update it regularly, but you can use this function to download the newest version. The download is >5MB, but this function will summarise the information into a smaller version and delete the original file.
Returns: the path to the data frame (invisibly)
E.9.4 FLoRA_date
Get date FLoRA was updated
FLoRA_date()Returns: the date
E.9.5 retractionwatch
RetractionWatch data
retractionwatch()
rw()DOIs and nature of statements from the RetractionWatch database. Use rw_date() to find the date it was downloaded, and rw_update() to update it.
Returns: a data frame
E.9.6 rw
RetractionWatch data
retractionwatch()
rw()DOIs and nature of statements from the RetractionWatch database. Use rw_date() to find the date it was downloaded, and rw_update() to update it.
Returns: a data frame
E.9.7 rw_update
Update retractionwatch
rw_update()metacheck comes with a built-in data frame called retractionwatch. We update it regularly, but you can use this function to download the newest version. The download is >50MB, but this function will summarise the information into a smaller version (~0.5 MB) and delete the original file.
Returns: the path to the data frame (invisibly)
E.9.8 rw_date
Get date retractionwatch was updated
rw_date()Returns: the date
E.10 Preregistration comparison (regcheck)
E.10.1 regcheck_compare
Compare a Preregistration with a Paper via RegCheck
regcheck_compare(
paper_text,
prereg_text = NULL,
registration_id = NULL,
client = c("ollama", "groq", "openai", "deepseek"),
base_url = NULL,
api_token = NULL,
dimensions = NULL,
reasoning_effort = "medium",
poll_interval = 5,
timeout = 3600
)Sends the full text of a paper and the text of its (pre)registration to a RegCheck server, which uses an LLM to compare them dimension by dimension (e.g., sample size, hypotheses, exclusion criteria) and judge for each dimension whether the paper deviates from the registration.
Returns: a data frame with one row per compared dimension, with columns dimension, deviation_judgement (“yes”, “no”, or “missing”), paper_summary, prereg_summary, deviation_information, paper_quotes, and prereg_quotes. The raw server result is attached as the “regcheck_result” attribute.
E.10.2 regcheck_tidy
Tidy a RegCheck result
regcheck_tidy(result)Converts the raw result of a RegCheck comparison (a list with an items element, one item per compared dimension) into a data frame.
Returns: a data frame with one row per dimension and columns dimension, deviation_judgement, paper_summary, prereg_summary, deviation_information, paper_quotes, and prereg_quotes
E.10.3 regcheck_base_url
RegCheck server URL for a client
regcheck_base_url(client = "ollama", base_url = NULL)Returns the base URL to use for a given RegCheck client. An explicit base_url always wins, then the REGCHECK_BASE_URL environment variable, then a client-specific default: the bundled local RegCheck server (http://localhost:8000) for "ollama", and the hosted RegCheck app for the API-based clients ("groq", "openai", "deepseek").
Returns: the base URL with any trailing slash removed
E.10.4 regcheck_setup_local
Set up the local RegCheck server (manual Python path)
regcheck_setup_local(python = NULL)Creates a Python virtual environment inside the bundled RegCheck app directory, installs all dependencies, and downloads the NLTK data. Only needed if you are using the manual Python path – if you have Docker, use regcheck_start_local() directly with method = "docker".
Returns: invisibly, the path to the virtual environment
E.10.5 regcheck_start_local
Start the local RegCheck server
regcheck_start_local(method = c("docker", "python"), model = NULL, port = 8000)Starts the bundled RegCheck server as a background process, either via Docker (recommended) or a manual Python virtual environment. The server runs at http://localhost:8000 and is used automatically by module_run(paper, "reg_check") (the default client = "ollama").
Returns: invisibly, the process object
E.10.6 regcheck_stop_local
Stop the local RegCheck server
regcheck_stop_local()Kills the background process started by regcheck_start_local().
Returns: invisibly NULL
E.11 Finding and downloading files from repository archives
E.11.1 osf_links
Find OSF Links in Papers
osf_links(paper)Get all OSF links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention like “osf.io/gms8z/” that the source PDF/HTML never encoded as an actual hyperlink (common in PDF-converted papers, where a plain-text URL mention loses its link formatting) — the url table only ever contains links the source document itself made clickable, so a repository this important cannot rely on that alone. Same two-tier approach github_links() already uses for GitHub.
Returns: a table with the OSF url in the first (href) column
E.11.2 osf_info
Retrieve info from the OSF by ID
osf_info(osf_url, id_col = 1, recursive = FALSE, pb = NULL, cache = FALSE)Repository listings are cached for the session (keyed by URL and recursive), so repeated calls do not re-query the OSF API; clear the cache with osf_cache_clear() or disable it with options(metacheck.osf.cache = FALSE).
Returns: a data frame of information
E.11.3 osf_file_download
Download all OSF Project Files
osf_file_download(
osf_id,
download_to,
max_file_size = NULL,
max_download_size = NULL,
max_folder_length = Inf,
ignore_folder_structure = FALSE,
mode = c("all", "select", "files", "zip"),
unzip = TRUE,
metadata = TRUE,
osf_pat = NULL,
pb = NULL
)Creates a directory for the OSF ID and downloads all of the files using a folder structure from the OSF project nodes and file storage structure. Returns (invisibly) a data frame with file info.
Returns: data frame of file info, one row per file (osf_id is each file’s own ID). It carries download_path, the absolute folder the project was saved in, plus osf_project and osf_url identifying the project, so the result can be passed straight to zenodo_upload(). downloaded, size_on_disk, and attempted report the verification described above.
In mode = "all" there is no file listing, so the table has one row per node instead: folder, osf_project, osf_url, title, files (how many arrived), bytes, download_path, and downloaded. It can still be passed to zenodo_upload(), which uses download_path.
E.11.4 osf_type
Get OSF GUID Type
osf_type(guid)Returns: the type, or "inaccessible" when the GUID is a validly-formed OSF ID but the resource itself could not be reached (private, embargoed, withdrawn, or deleted) – distinct from NA, which means the input was not a valid OSF ID at all
E.11.5 osf_check_id
Check OSF IDs
osf_check_id(osf_id)Check if strings are valid OSF IDs, URLs, or waterbutler IDs. Basically an improved wrapper for osfr::as_id() that returns NA for invalid IDs in a vector.
Returns: a vector of valid IDs, with NA in place of invalid IDs
E.11.6 osf_api_check
Check OSF API Server Status
osf_api_check(
osf_api = getOption("metacheck.osf.api"),
on_error = c("stop", "warn", "ignore")
)Check the status of the OSF API server.
Returns: the OSF status
E.11.7 osf_delay
Set the OSF delay
osf_delay(delay = NULL)Sometimes the OSF gets fussy if you make too many calls, so you can set a delay of a few seconds before each call. Use osf_delay() to get or set the OSF delay.
Returns: the delay in seconds (numeric); invisibly when setting it
E.11.8 osf_get_all_pages
Get All OSF API Query Pages
osf_get_all_pages(url, page_end = Inf)OSF API queries only return up to 10 items per page, so this helper functions checks for extra pages and returns all of them
Returns: a table of the returned data. When the request could not be completed (e.g. a private, embargoed, withdrawn, or deleted resource, or a network failure), this is an empty list() with an osf_error attribute set to "forbidden", "not_found", "gone", or "request_failed" (a bare NULL cannot carry attributes in R, so it would silently discard this) – callers that only check length(result) == 0 keep working unchanged, and callers that need to tell “inaccessible” apart from “genuinely empty” can check attr(result, "osf_error").
E.11.9 osf_preprint_list
Get A list of preprints from the OSF
osf_preprint_list(
provider = NULL,
date_created = NULL,
date_modified = NULL,
page_start = 1,
page_end = page_start
)Returns: a table of preprint info
E.11.10 osf_pat
Set or get the OSF personal access token
osf_pat(pat = NULL)Use osf_pat() to get the token used to authorise OSF API requests, or osf_pat("your-token") to set it for the rest of the session.
Returns: the current token (character; "" when none is set)
E.11.11 osf_app
Launch OSF Download App
osf_app(quiet = FALSE, ...)Launch the OSF app: enter an OSF user or project ID, search and tick the projects you want, then download them with osf_file_download().
Returns: NULL (invisibly)
E.11.12 osf_cache_clear
Clear the session cache of OSF listings
osf_cache_clear()Returns: the number of cached listings removed, invisibly
E.11.13 osf_user_projects
List the Projects Belonging to an OSF User
osf_user_projects(user_id, pb = NULL)Every project an OSF user contributes to, as a table you can read and filter before downloading anything. Pass the whole table, or any subset of it, to osf_file_download().
Returns: a data frame with one row per project: osf_id, name, category, public, and osf_url. Zero rows when the user has no projects that could be listed.
E.11.14 github_links
Find GitHub Links in Papers
github_links(paper)GitHub links can be in PDFs in several ways.
Returns: a table with the GitHub url in the first (text) column
E.11.15 github_info
Get GitHub Repo Info
github_info(repo, recursive = FALSE)Returns: a list of information about the repo
E.11.16 github_repo
Get Short GitHub Repo Name
github_repo(repo)Returns: character string of short repo name
E.11.17 github_files
Get File List from GitHub
github_files(repo, dir = "", recursive = FALSE)Returns: a data frame of files
E.11.18 github_pat
Set or get a GitHub token
github_pat(pat = NULL)A token raises GitHub’s limit from 60 to 5,000 requests per hour. A fine-grained token with access to public repositories only reads nothing that is not public: make one at https://github.com/settings/personal-access-tokens/new, choose “Public repositories” under “Repository access”, and add no permissions.
Returns: the current token (character; "" when none is set)
E.11.19 github_languages
Get Languages from GitHub Repo
github_languages(repo)Returns: vector of languages
E.11.20 github_readme
Get README from GitHub
github_readme(repo)Returns: a character string of the README contents
E.11.21 github_tree_files
Get GitHub repository files via the Git Trees API
github_tree_files(repo)Fetches the complete file tree of a GitHub repository in two API calls (repo metadata + recursive tree), rather than the N-request recursive /contents/ crawl used by github_files().
Returns: a list with:
gatedlogical;TRUEonly when the repository could not be listed at all (invalid/inaccessible, or a truncated tree)reasoncharacter reason, orNAfilesdata.frame shaped likegithub_files()output, orNULLwhen the tree could not be fetcheddefault_branchcharacterlicensecharacter licence key, or NA
E.11.22 zenodo_links
Find Zenodo Links in Papers
zenodo_links(paper)Get all Zenodo links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention (a DOI like “10.5281/zenodo.1234567” is routinely cited without any URL scheme at all) that the source PDF/HTML never encoded as an actual hyperlink — the url table only ever contains links the source document itself made clickable. Same two-tier approach github_links() uses for GitHub.
Returns: a table with the Zenodo url in the first (text) column
E.11.23 zenodo_info
Retrieve info from Zenodo by URL
zenodo_info(zenodo_url, id_col = 1, pb = NULL, cache = FALSE)Returns: a data frame of information
E.11.24 zenodo_file_download
Download all Zenodo Project Files
zenodo_file_download(
zenodo_id,
download_to,
max_file_size = 10,
max_download_size = 100,
unzip_types = NULL,
pb = NULL
)Creates a directory for the Zenodo ID and downloads all of the files using a folder structure from the Zenodo project nodes and file storage structure. Returns (invisibly) a data frame with file info.
Returns: data frame of file info. When members are extracted from a zip, its row reports the zip with extracted giving the number of members written; size_on_disk and checksum_ok are then NA, because what is on disk is the members rather than the archive Zenodo published a size and MD5 for.
E.11.25 zenodo_pat
Set or get the Zenodo personal access token
zenodo_pat(pat = NULL, sandbox = TRUE)Use zenodo_pat() to get the token used to authorise Zenodo uploads, or zenodo_pat("your-token") to set it for the rest of the session. The sandbox and the real Zenodo are separate services with separate accounts, so they need separate tokens and are stored separately here.
Returns: the current token (character; "" when none is set)
E.11.26 zenodo_upload
Upload Folders to Zenodo
zenodo_upload(
folders,
sandbox = TRUE,
zenodo_pat = NULL,
publish = FALSE,
license = "cc-by-4.0",
upload_type = "dataset",
metadata = NULL,
as_zip = TRUE,
split_materials = "materials",
upload_osf_metadata = TRUE,
max_file_size = NULL,
ask = TRUE,
pb = NULL
)Creates a Zenodo deposition for each folder, uploads every file in it, and attaches metadata. Designed to take the output of osf_file_download() directly, so a whole OSF account can be archived on Zenodo in two steps:
Returns: a data frame with one row per folder: the folder path, the Zenodo deposition id, its DOI, the URL to review it, how many files were uploaded and skipped, and whether it was published
E.11.27 rbox_links
Find ResearchBox Links in Papers
rbox_links(paper)Get all ResearchBox links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention like “researchbox.org/ 801” that the source PDF/HTML never encoded as an actual hyperlink (common in PDF-converted papers, where a plain-text URL mention loses its link formatting) — the url table only ever contains links the source document itself made clickable. Same two-tier approach github_links() uses for GitHub.
Returns: a table with the ResearchBox url in the first (href) column
E.11.28 rbox_info
Retrieve info from ResearchBox by URL
rbox_info(rb_url, id_col = 1, pb = NULL, cache = FALSE)With an API key set (see rbox_token()), each box costs two API requests: its details and its file list. A peer-review link’s passcode is sent along. Without a key, or when the API refuses (ResearchBox limits how often the same box can be requested per hour), the box’s web page is read instead.
Returns: a data frame of information
E.11.29 rbox_files
List the Files in a ResearchBox
rbox_files(rb_url, pb = NULL, cache = FALSE)Lists every file in one or more boxes without downloading them, using one API request per box. A peer-review link’s passcode is sent along. Needs an API key (see rbox_token()).
Returns: a data frame with one row per file: rb_url, name, file_id, section, type, description, extension, size (bytes) and md5, plus an error column when a box could not be listed
E.11.30 rbox_search
Search ResearchBox
rbox_search(q, type = c("data", "code", "paper"), limit = 100, offset = 0)Searches the data, code or papers in ResearchBox’s public boxes. A data search returns one row per matching variable, a code search one row per matching file, and a paper search one row per matching box. Needs an API key (see rbox_token()).
Returns: a data frame with one row per hit: rb_url and the columns ResearchBox gives for that type (box_id, formatted_reference, and for data file_id, var_id, var_name, var_description, original_name and upload_time; for code file_id, original_name and upload_time; for papers box_title). The attribute "total" is the number of hits ResearchBox found, which can be more than were returned.
E.11.31 rbox_file_download
Retrieve files from ResearchBox by URL
rbox_file_download(
rb_url,
pb = NULL,
file_ids = "all",
subfolders = c("type", "section"),
cache = FALSE
)Downloads the boxes as zips and unpacks them into the repository cache (see repo_cache_dir()), so a later call for the same box reuses the files without asking ResearchBox again.
Returns: a data frame with one row per file (rb_url, name, file_location, size, isdir, checksum_ok, plus the file type columns of file_types), and a "failed" attribute: a data frame with rb_url, name (NA when the whole box failed) and error for each box or file that could not be retrieved. An error starting “API rate limit exhausted:” is expected to succeed when run again later.
E.11.32 rbox_token
Get or Set the ResearchBox API Key
rbox_token(token = NULL)ResearchBox’s API (https://researchbox.org/api_call.php) needs a personal key, which you can make at https://researchbox.org/api. metacheck reads it from the RESEARCHBOX_API_KEY environment variable, which you can set in your .Renviron file (usethis::edit_r_environ()):
Returns: the current key (character; "" when none is set)
E.11.33 aspredicted_links
Find AsPredicted Links in Papers
aspredicted_links(paper)Returns: a table with the AsPredicted url in the first (href) column
E.11.34 aspredicted_info
Retrieve info from AsPredicted by URL
aspredicted_info(ap_url, id_col = 1, wait = 1, cache = FALSE)With a ResearchBox API key set (see rbox_token()), each preregistration is read through ResearchBox’s API, which serves AsPredicted’s public preregistrations, with one request each. Without a key, or when the API fails, the preregistration’s web page on AsPredicted is read instead; AsPredicted asks for a captcha after several pages, and no page is read after that.
Returns: a data frame of information
E.11.35 dataverse_links
Find Dataverse Links in Papers
dataverse_links(paper)Get all Dataverse links: real hyperlinks from the paper’s own url table, matched against a list of known Dataverse installations (see dataverse_links()’s source for the list; Dataverse is open-source software run by many independent hosts, unlike Zenodo, so there is no single hostname or DOI prefix to match against everything). Same two-tier hyperlink + bare-mention approach zenodo_links() uses, applied per known host.
Returns: a table with the Dataverse url in the first (text) column
E.11.36 dataverse_info
Retrieve info from Dataverse by URL
dataverse_info(dataverse_url, id_col = 1, pb = NULL, cache = FALSE)Returns: a data frame of information
E.11.37 dataverse_file_download
Download all files from a Dataverse dataset
dataverse_file_download(
host,
doi,
download_to,
max_file_size = 10,
max_download_size = 100,
unzip_types = NULL,
pb = NULL
)Creates a directory for the dataset and downloads all of its files. Returns (invisibly) a data frame with file info.
Returns: data frame of file info. When members are extracted from a zip, its row reports the zip with extracted giving the number of members written; size_on_disk and checksum_ok are then NA, because what is on disk is the members rather than the archive Dataverse published a size and MD5 for.
E.11.38 dataverse_pat
Set or get a Dataverse API token
dataverse_pat(host, pat = NULL)Dataverse installations each issue their own API token (unlike Zenodo/OSF, which are single services) – a token from dataverse.harvard.edu is meaningless to dataverse.nl. Tokens are therefore stored per host.
Returns: the current token (character; "" when none is set)
E.11.39 dryad_links
Find Dryad Links in Papers
dryad_links(paper)Get all Dryad links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention (a DOI like “10.5061/dryad.j1fd7” is routinely cited without any URL scheme at all) that the source PDF/HTML never encoded as an actual hyperlink – the url table only ever contains links the source document itself made clickable. Same two-tier approach zenodo_links() uses.
Returns: a table with the Dryad url in the first (text) column
E.11.40 dryad_info
Retrieve info from Dryad by URL
dryad_info(dryad_url, id_col = 1, pb = NULL, cache = FALSE)Returns: a data frame of information, one row per link, with the dataset’s metadata, its file records in the list column files, and storage_size (the dataset’s total size in bytes as Dryad reports it), files_total (the number of files Dryad reports), and files_incomplete (TRUE when fewer files could be listed than Dryad reports; a warning is given too). A sharing link (datadryad.org/stash/share/...) is followed to the dataset it leads to; one that leads nowhere has its reason in error, as a dataset that could not be found does.
E.11.41 dryad_file_download
Download all files from a Dryad dataset
dryad_file_download(
dryad_doi,
download_to,
max_file_size = 10,
max_download_size = 100,
unzip_types = NULL,
pb = NULL
)Creates a directory for the dataset and downloads all of its files. Returns (invisibly) a data frame with file info.
Returns: data frame of file info. When members are extracted from a zip, its row reports the zip with extracted giving the number of members written; size_on_disk and checksum_ok are then NA, because what is on disk is the members rather than the archive Dryad published a size and digest for.
E.11.42 dryad_pat
Set or get a Dryad API token
dryad_pat(pat = NULL)Dryad issues OAuth2 bearer tokens from account settings. Unlike Figshare, Dataverse, Zenodo, and OSF, a token is REQUIRED to download file bytes even from a fully public dataset (verified live 2026-08-16: both the per-file and whole-dataset download endpoints answer 401 with no token, though dataset metadata and file listings are readable without one). Without a token, dryad_file_download() can list what a dataset contains but cannot fetch any of it.
Returns: the current token (character; "" when none is set)
E.11.50 researchdata4tu_links
Find 4TU.ResearchData Links in Papers
researchdata4tu_links(paper)Get all 4TU.ResearchData links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention (a DOI like “10.4121/16766929” is routinely cited without any URL scheme at all) that the source PDF/HTML never encoded as an actual hyperlink – the url table only ever contains links the source document itself made clickable. Same two-tier approach zenodo_links() uses.
Returns: a table with the 4TU.ResearchData url in the first (text) column
E.11.51 researchdata4tu_info
Retrieve info from 4TU.ResearchData by URL
researchdata4tu_info(researchdata4tu_url, id_col = 1, pb = NULL, cache = FALSE)Thin wrapper around figshare_info() with host = "data.4tu.nl" – see the note at the top of this file for why 4TU.ResearchData’s Djehuty platform can reuse the Figshare-family implementation directly.
Returns: a data frame of information
E.11.52 researchdata4tu_file_download
Download all files from a 4TU.ResearchData article
researchdata4tu_file_download(
researchdata4tu_id,
download_to,
max_file_size = 10,
max_download_size = 100,
unzip_types = NULL,
pb = NULL
)Thin wrapper around figshare_file_download() with host = "data.4tu.nl" – see the note at the top of this file. Creates a directory for the article and downloads all of its files. Returns (invisibly) a data frame with file info.
Returns: data frame of file info. When members are extracted from a zip, its row reports the zip with extracted giving the number of members written; size_on_disk and checksum_ok are then NA, because what is on disk is the members rather than the archive 4TU.ResearchData published a size and MD5 for.
E.11.53 researchdata4tu_pat
Set or get a 4TU.ResearchData API token
researchdata4tu_pat(pat = NULL)4TU.ResearchData issues its own personal access tokens (from account settings on data.4tu.nl) – a Figshare.com token is meaningless here, so this is stored separately from figshare_pat(). A token is optional for reading public articles (used here only to raise rate limits or read private ones).
Returns: the current token (character; "" when none is set)
E.11.54 psycharchives_links
Find PsychArchives Links in Papers
psycharchives_links(paper)Get all PsychArchives links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention (a handle like “20.500.12034/17526” is routinely cited without any URL scheme at all) that the source PDF/HTML never encoded as an actual hyperlink — the url table only ever contains links the source document itself made clickable. Same two-tier approach github_links() uses for GitHub.
Returns: a table with the PsychArchives url in the first (href) column
E.11.55 psycharchives_info
Retrieve info from PsychArchives by URL
psycharchives_info(pa_url, id_col = 1, pb = NULL, cache = FALSE)Returns: a data frame of information
E.11.56 psycharchives_file_download
Retrieve public file list from PsychArchives by URL
psycharchives_file_download(pa_url, pb = NULL, cache = FALSE)Lists the publicly retrievable bitstreams of one or more PsychArchives items via the DSpace REST API, without downloading them. Each row carries an absolute file_url (the bitstream retrieve endpoint) so the actual bytes are fetched later by download_repo_files(), the same deferred path used for Zenodo and OSF. Restricted files are omitted by the API and never appear here.
Returns: a data frame of file information (one row per public bitstream)
E.11.57 gitlab_links
Find GitLab Links in Papers
gitlab_links(paper)GitLab.com links can appear in papers the same two ways GitHub links do: a real hyperlink, or a bare “owner/repo” mention near the word “gitlab”. Only gitlab.com is matched; a self-hosted GitLab instance’s own domain is indistinguishable from an arbitrary institutional website (see the file header comment).
Returns: a table with the GitLab url in the first (text) column
E.11.58 gitlab_pat
Set or get a GitLab API token
gitlab_pat(pat = NULL)A personal access token from gitlab.com account settings. Optional for listing/downloading PUBLIC projects (used here only to raise GitLab’s unauthenticated rate limit); required for private projects.
Returns: the current token (character; "" when none is set)
E.11.59 gitlab_repo
Get Short GitLab Project Path
gitlab_repo(repo)Returns: character string of the “namespace/project” path, or NULL if the project does not exist / is not reachable
E.11.60 gitlab_tree_files
Get GitLab project files, license, and default branch
gitlab_tree_files(repo)Fetches a GitLab project’s file tree (following pagination, since GitLab’s tree endpoint caps at 100 entries per page, unlike GitHub’s single-request Git Trees API), its detected licence, and default branch. File sizes are backfilled with a single batched GraphQL repository.blobs(paths: [...]) query, since the REST tree listing does not include size.
Returns: a list with:
gatedlogical;TRUEonly when the project could not be listed at all (invalid/inaccessible)reasoncharacter reason, orNAfilesdata.frame with repo/clean_repo/name/path/ download_url/size/type, orNULLwhen the tree could not be fetcheddefault_branchcharacterlicensecharacter SPDX-style key (e.g. “mit”), or NA
E.11.61 dspace_links
Find DSpace (legacy REST API) Links in Papers
dspace_links(paper)Get all links to any of the legacy-DSpace-REST installations in .dspace_legacy_hosts() (PsychArchives plus the others verified 2026-09-11 – see the note at the top of this file): real hyperlinks from the paper’s own url table, plus a body-text fallback. The fallback recognises a bare mention (no hostname attached) only for PsychArchives’ own handle prefix (20.500.12034) – see .dspace_legacy_parse() for why the other installations’ bare mentions cannot be resolved the same way. This is the general (all-hosts) counterpart to psycharchives_links(), which stays PsychArchives-only for anyone calling it directly.
Returns: a table with the DSpace url in the first (href) column
E.11.62 dspace7_links
Find DSpace 7+ Links in Papers
dspace7_links(paper)Get all links to any of the DSpace 7 (or later) installations in .dspace7_hosts() – see the note at the top of this file for how they were found and why they need a separate implementation from dspace_links() (legacy DSpace, archive-psycharchives.R). Real hyperlinks from the paper’s own url table, plus a body-text fallback for a bare mention that still carries one of these hosts (a bare handle prefix with no host at all cannot be attributed to a specific installation – see .dspace7_parse()).
Returns: a table with the DSpace 7 url in the first (href) column
E.11.63 dspace7_file_download
Retrieve public file list from a DSpace 7+ installation by URL
dspace7_file_download(dspace7_url, pb = NULL)Lists the publicly retrievable files (the item’s ORIGINAL bundle) of one or more DSpace 7 items via the REST API, without downloading them. Each row carries an absolute file_url so the actual bytes are fetched later by download_repo_files, the same deferred path used for Zenodo, OSF, and legacy DSpace (psycharchives_file_download()).
Returns: a data frame of file information (one row per public file)
E.11.64 dataone_links
Find DataONE Links in Papers
dataone_links(paper)Get all DataONE links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE DOI mention (routinely cited without any URL scheme at all) using the DOI prefixes of known hosts (see .dataone_hosts()) – the url table only ever contains links the source document itself made clickable. Same two-tier approach zenodo_links() uses.
Returns: a table with the DataONE url in the first (text) column
E.11.65 dataone_info
Retrieve info from DataONE by URL
dataone_info(dataone_url, id_col = 1, pb = NULL, cache = FALSE)Returns: a data frame of information
E.11.66 mendeley_links
Find Mendeley Data Links in Papers
mendeley_links(paper)Get all Mendeley Data links: real hyperlinks from the paper’s own url table, plus a body-text fallback for a BARE mention (a DOI like “10.17632/vjtxybrc28.1” is routinely cited without any URL scheme at all) that the source PDF/HTML never encoded as an actual hyperlink – the url table only ever contains links the source document itself made clickable. Same two-tier approach zenodo_links() uses.
Returns: a table with the Mendeley Data url in the first (text) column
E.11.67 mendeley_info
Retrieve info from Mendeley Data by URL
mendeley_info(mendeley_url, id_col = 1, pb = NULL, cache = FALSE)Returns: a data frame of information
E.11.68 ldbase_links
Find LDbase links in a paper
ldbase_links(paper)Finds links to LDbase (https://ldbase.org) projects, datasets, code and documents, as hyperlinks or in the text, and LDbase DOIs (prefix 10.33009, e.g. 10.33009/ldbase.1627053448.d151). LDbase’s own DOI (10.33009/ldbase, cited for the repository itself) and links to its home or search pages are not included: they name no deposit.
Returns: a data frame with a row for each link (href, plus text_id / paper_id), with ldbase_url (the link) and ldbase_type (projects, datasets, code, documents, or doi for a DOI not yet resolved)
E.11.69 ldbase_info
Get the files of LDbase projects, datasets, code and documents
ldbase_info(ldbase_url, id_col = 1, pb = NULL, cache = FALSE)Lists the files of each LDbase link: for a project, every dataset, code and document in its hierarchy (child projects included); for a single dataset, code or document, its own file. A DOI is first resolved to the page it leads to. LDbase has no API, so this reads the public pages (one request per project or item). Items stored elsewhere (LDbase’s “external link” option) have no file here and are not listed.
Returns: a data frame with one row per link: ldbase_url, ldbase_page (the resolved page), ldbase_type, title, doi, license (the licence of the item, or every distinct licence of a project’s items joined by “;”), a list-column files (a data frame per link with file_name, file_path, file_url, item_type, item_url) and error (NA, or why the link could not be listed)
E.11.70 fsd_links
Find FSD Links in Papers
fsd_links(paper)Get all Finnish Social Science Data Archive (FSD / Aila Data Service) links: real hyperlinks from the paper’s own url table (the catalogue page, a 10.60686 DOI, or a urn.fi URN), plus a body-text fallback for a bare “FSD####” mention that the source PDF/HTML never encoded as an actual hyperlink. Same two-tier approach github_links() uses for GitHub.
Returns: a table with the FSD url in the first (href) column
E.11.71 fsd_info
Retrieve info from FSD by URL
fsd_info(fsd_url, id_col = 1, pb = NULL, cache = FALSE)Lists a Finnish Social Science Data Archive study’s title, DOI, access restriction, and data-file inventory (name, format, case/variable counts) via its open DDI-C 2.5 XML metadata record. There is no file-level download here – the DDI record carries no download URI, and actual data access requires Haka/Aila login or a per-dataset access application (see the file header comment) – so files is for REPORTING what a study contains, not for download_repo_files() to fetch.
Returns: a data frame of information
E.11.72 dryad_auth
Set or get Dryad OAuth2 client credentials
dryad_auth(
client_id = NULL,
client_secret = NULL,
token_url = "https://datadryad.org/oauth/token"
)Dryad also issues a client id/secret pair (from the same account settings page as the plain token in dryad_pat()), meant for the OAuth2 client-credentials grant. Unlike a plain token, which expires after 10 hours and must be regenerated by hand, a client id/secret is exchanged for a short-lived access token automatically – and refreshed automatically, for as long as the credentials themselves stay valid – every time a request needs one, via httr2::req_oauth_client_credentials(). This is the right choice for a long-running corpus build; dryad_pat() remains available for a single quick session where generating a client id/secret is not worth the extra step.
Returns: invisibly, the httr2::oauth_client() built from the current credentials, or NULL when none are set
E.11.73 local_files
List Local Files
local_files(path, recursive = FALSE)Lists all files in a local directory recursively and returns a data frame compatible with the repo_check output table, for use with code_check.
Returns: a data frame with columns repo_url, file_name, file_url, file_location, file_size, file_type
E.11.74 download_repo_files
Download the files listed by repo_check()
download_repo_files(
files,
max_file_size = 100,
max_download_size = 500,
max_files_per_repo = Inf,
repo_file_counts = NULL,
zip_timeout_s = 120,
cache = FALSE,
skip_on_api_limit = FALSE,
pb = NULL
)Fetches the bytes for a table of repository files (as produced by repo_check()), writing each into a per-session temp directory or a persistent on-disk cache, and fills in file_location for every file it successfully retrieves. For Dryad (stash:download endpoint), GitHub (zipball) and GitLab (archive.zip), a whole-repo archive download is used instead of one HTTP request per file. None of these archives reports its size in advance, so the size is estimated from the listed file sizes: an archive with no listed sizes, or an estimate above twice max_download_size, is not used, and a download that grows past twice max_download_size is stopped. A repo whose archive is not used or fails falls back to file-by-file fetching automatically, as do all other hosts.
Returns: files with file_location filled in for every file retrieved (unchanged, i.e. NA, for files that were skipped or failed), and three attributes: "gated" (data.frame: repo_url, message — repositories refused outright by the size caps), "oversize_skipped" (data.frame: repo_url, file_name, file_size — individual files skipped under max_file_size), and "failed" (data.frame: repo_url, file_name, file_url, paper_id, error — files whose download was attempted but errored, e.g. a transient network failure, or — when skip_on_api_limit = TRUE and the host confirmed it — an exhausted API rate limit; see skip_on_api_limit. file_url is carried along so a caller can point someone at the file directly instead of re-deriving the link. paper_id is files$paper_id[i] when files carries that column (e.g. a paperlist batch), NA otherwise — lets a caller attribute a failure back to the paper it belongs to without re-joining on repo_url).
E.11.75 repo_cache_clear
Delete the downloaded-repository file cache
repo_cache_clear(repo_url = NULL, quiet = FALSE)Removes files fetched from data repositories by data_check / code_check from the on-disk cache (see repo_cache_dir()). The cache only speeds up re-runs — anything deleted is simply re-downloaded when next needed — so clearing it is always safe; the usual reason is to reclaim disk space after a large corpus build.
Returns: the number of bytes freed (numeric), invisibly.
E.11.76 repo_cache_dir
Locate the downloaded-repository file cache
repo_cache_dir()Files fetched from OSF/GitHub/Zenodo/ResearchBox by data_check and code_check with cache = TRUE are stored in a lasting on-disk cache so they are reused across modules and R sessions (never re-downloaded). This returns that cache’s root directory. With cache = FALSE (the default) the files are kept in a temporary folder that R removes when the session ends.
Returns: the cache root directory path (character), invisibly
E.11.77 repo_cache_size
Size of the downloaded-repository file cache
repo_cache_size()Returns: total size of the cache in bytes (numeric). 0 when the cache is empty or absent.
E.11.78 repo_info_cache_clear
Clear the on-disk repository-listing cache
repo_info_cache_clear()Deletes all cached repository listings. Safe at any time – a cleared entry is simply re-fetched from the host’s API the next time it is needed.
Returns: the number of cache entries removed, invisibly
E.12 Asking a large language model
E.12.1 llm
Query an LLM
llm(
text,
system_prompt,
type = NULL,
text_col = "text",
model = llm_model(),
params = list(),
phase = NULL,
capture_reasoning = FALSE
)Ask a large language model (LLM) any question you want about a vector of text or the text from a text_search(). When type is provided, uses ellmer’s structured output API to guarantee output conforming to the type spec; otherwise returns free-text responses in an answer column.
Returns: a data frame of results
E.12.2 llm_use
Set or get metacheck LLM use
llm_use(llm_use = NULL)Mainly for use in optional LLM workflows in modules
Returns: the current option value (logical)
E.12.3 llm_model
Set the default LLM model
llm_model(model = NULL)Use llm_model_list() to get a list of available models
Returns: the name of the default model, or NULL when none is set; invisibly when setting it
E.12.4 llm_model_list
List LLM Models
llm_model_list(platform = NULL)List available LLM models for the specified platform.
Returns: a data frame of models and info
E.12.5 llm_max_calls
Set the maximum number of calls to the LLM
llm_max_calls(n = NULL)Returns: the maximum number of calls (integer); invisibly when setting it
E.12.6 llm_max_tokens
Set the default max_tokens for LLM calls
llm_max_tokens(n = NULL)llm() needs SOME max_tokens value on every call (a provider truncates a response with no clear error otherwise – a codebook/data-check pass over many columns at once is the case most likely to need more than the built-in default). Rather than requiring every module call to repeat params = list(max_tokens = ...) individually, set it once per session here; llm() falls back to this whenever a caller’s own params does not already specify max_tokens (an explicit params$max_tokens on a single call still wins, same precedence as every other params entry).
Returns: the current default max_tokens value (invisibly when setting)
E.12.7 llm_reasoning
Set the default reasoning effort for LLM calls
llm_reasoning(effort = NULL)Every LLM call this package makes is a bounded, structured-extraction task over text already given in the prompt (classify a file, extract a variable’s label, list the scales named in ~30 sentences, group files by study) – pattern-matching over given material, not multi-step reasoning (no arithmetic, no multi-hop inference, no planning). A “thinking”/ “reasoning” model spending extra tokens re-deriving what the prompt already states is close to pure overhead here: it inflates cost/latency and eats into the same max_tokens budget as the visible answer, which is a confirmed real cause of truncated structured responses on gpt-oss models via Groq. Minimal reasoning effort is the right DEFAULT for what this package does, not a tradeoff against quality.
Returns: the current default reasoning effort (invisibly when setting)
E.12.8 llm_timeout
Set the per-call timeout for LLM requests
llm_timeout(seconds = NULL)Every single request llm() makes (one item, or one batch, depending on the caller) is bounded by this wall-clock limit. Without it, a stuck local model (most often Ollama under load, or a batch too large for the model to finish) blocks silently – no log output, no progress, nothing – for as long as the underlying HTTP client is willing to wait, which can be hours. A timeout turns that invisible stall into a fast, visible, per-row failure (recorded the same way a provider error already is – see llm()’s own error handling), so a large batch run fails fast and retries instead of hanging.
Returns: the current timeout in seconds (invisibly when setting)
E.12.9 llm_cache
Turn the lasting LLM response cache on or off, or query it
llm_cache(enabled = NULL)llm() stores each structured or free-text response and replays it on identical later calls, so asking the same question again does not re-issue (or re-bill) the LLM request. Because llm() runs at temperature 0 the replayed answer matches a fresh call. Cached entries are keyed by model, system prompt, input text, type spec, and params; changing any of these produces a fresh call.
Returns: the current setting (logical), invisibly when setting
E.12.10 llm_cache_clear
Clear the on-disk LLM response cache
llm_cache_clear()Deletes all cached LLM responses in the cache that is in use: the lasting cache when llm_cache() is TRUE, otherwise this session’s temporary one.
Returns: the number of cache entries removed, invisibly
E.12.11 metacheck_cache_info
Show where metacheck’s caches live and how big they are
metacheck_cache_info()Prints the location and size of all three on-disk caches — the downloaded repository-FILE cache, the repository-LISTING cache (dataset/record metadata, off by default – see repo_info_cache_clear()), and the LLM-response cache — so they are never hidden. Files are kept between sessions only when you ask for it (cache = TRUE in a module, or llm_cache(TRUE)); each lasting cache then sits in a folder in the current working directory (.metacheck_repo_cache, .metacheck_repo_info_cache, and .metacheck_llm_cache); relocate all three with options(metacheck.cache.dir = "/some/path"). While llm_cache() is FALSE, the LLM row shows this session’s temporary folder.
Returns: a data.frame (cache, path, size_mb), invisibly.
E.16 Importing and exporting statistical output
E.16.1 import_jasp
Read a JASP (.jasp) file
import_jasp(path)Extracts the dataset, its variable metadata (measurement level, value labels) and the list of analyses stored in a .jasp archive. Handles both the legacy binary format and the modern embedded-SQLite format.
Returns: a list with data (a data.frame; labelled columns carry haven-style label/labels attributes), columns (a data.frame of name and type), analyses (the parsed analyses.json, or NULL), format ("binary" or "sqlite"), and data_file_path (the original source path recorded in the archive, or NA).
E.16.2 import_omv
Read a jamovi (.omv) file
import_omv(path)Extracts the dataset, its variable metadata (measurement level, value labels) and a summary of the analyses stored in a .omv archive. The jamovi counterpart of import_jasp(), returning the same structure so downstream code (codebook extraction, data checks) treats an .omv like a .jasp or .sav.
Returns: a list with data (a data.frame; labelled columns carry haven-style label/labels attributes), columns (a data.frame of name and type), analyses (a character vector, one entry per analysis: its name and, when recoverable, the reproducible R syntax), format ("jamovi"), and data_file_path (NA; jamovi does not record the original import path).
E.16.3 import_mplus_output
Read an Mplus (.out) output file
import_mplus_output(path)Splits the file into its ALL-CAPS-headed sections (Mplus’s own structural marker – see the file header) and extracts each section’s fixed-width result tables and label/value statistics. The INPUT INSTRUCTIONS section (Mplus’s own verbatim analysis syntax) is skipped here – see .mplus_export_syntax() for recovering it separately as a sibling .inp file, the same way .spv_export_syntax() recovers .sps from .spv.
Returns: a list of result tables, each list(analysis, title, data, syntax, table_index) – the same shape read_stat_tables() returns for .jasp/.omv/.spv, so all formats can be processed identically downstream. analysis is the section title (e.g. "MODEL RESULTS"); syntax is the file’s own recovered INPUT INSTRUCTIONS block (the same text on every table, since one .out file is one Mplus run). Empty list if the file has no recoverable section.
E.16.4 import_stata_smcl
Read a Stata Markup and Control Language (.smcl) output log
import_stata_smcl(path)Renders the file’s markup to plain text (see .smcl_render()), splits it into one chunk per Stata command (the {com}. <command> echo, mirroring read_r_output()’s R-console-echo splitting), and extracts each chunk’s result tables and one-line statistics. graph export "....png" commands are recovered as ordinary command text; the PNG itself is never embedded in a .smcl file (Stata writes it to disk separately), so no chart data exists in this format to extract (see the file header).
Returns: a list of result tables, each list(analysis, title, data, syntax, table_index) – the same shape read_stat_tables() returns for .jasp/.omv/.spv, so all four formats can be processed identically downstream. analysis is the Stata command that produced the table (e.g. "summarize communication_index barrier_index"); syntax duplicates analysis (Stata’s own echoed command IS its syntax, unlike .spv where syntax is recovered separately from a different structure). Empty list if the file has no recoverable command output.
E.16.5 import_spv
Read the statistical result tables from an SPSS Viewer (.spv) file
import_spv(path)Ties the structure reader (.spv_read_structure(), which table exists, what analysis produced it, which format its data is in) and the two decoders (.spv_decode_light_table() for modern tables, .spv_decode_legacy_data() + .spv_decode_legacy_table() for pre-21 tables) into the SAME shape read_stat_tables() already returns from JASP’s analyses.json and jamovi’s protobuf blobs.
Returns: a list of result tables, each list(analysis, title, data, syntax, table_index) — the same shape read_stat_tables() returns for .jasp/.omv (syntax is .spv-specific: the exact SPSS syntax that produced the table, when recoverable). Empty list if the archive has no structure XML or no tables decode.
E.16.6 export_jasp_html
Export a JASP (.jasp) file’s own rendered output as standalone HTML
export_jasp_html(path, out = NULL)A .jasp archive already bundles a fully rendered index.html – JASP’s own output view, complete with result tables and any plots – alongside the data (see the file header). This extracts that index.html as-is and inlines every plot it references (an <img src="resources/.../*.png">) as a base64 data: URI, so the result is a single, portable, self-contained file that looks exactly like JASP’s own output window, with no external image files to keep alongside it.
Returns: the path written to, invisibly
E.16.7 export_omv_html
Export a jamovi (.omv) file’s own rendered output as standalone HTML
export_omv_html(path, out = NULL)A .omv archive already bundles a fully rendered index.html – jamovi’s own output view, complete with result tables and any plots – alongside the data (see the file header). This extracts that index.html as-is and inlines every plot it references (an <img src="....png"> under one of the per-analysis resources/ folders) as a base64 data: URI, so the result is a single, portable, self-contained file that looks exactly like jamovi’s own output window, with no external image files to keep alongside it.
Returns: the path written to, invisibly
E.16.8 export_mplus_html
Export an Mplus (.out) output file as standalone HTML
export_mplus_html(path, out = NULL)Builds an HTML page from what import_mplus_output() already decodes: one heading per section (SUMMARY OF ANALYSIS, MODEL FIT INFORMATION, MODEL RESULTS, …) and one <table> per detected result. Like export_stata_smcl_html(), this has no figures to embed: Mplus never writes chart data into the .out text file itself (see the file header).
Returns: the path written to, invisibly
E.16.9 export_stata_smcl_html
Export a Stata (.smcl) output log as standalone HTML
export_stata_smcl_html(path, out = NULL)Builds an HTML page from what import_stata_smcl() already decodes: one heading per Stata command (its exact syntax, since a .smcl command echo IS the syntax) and one <table> per detected result. Unlike export_jasp_html()/export_omv_html() (which re-export an already-rendered view) or export_spv_html() (which renders decoded charts as images), this has no figures to embed at all: a .smcl file never contains chart image data (see the file header) – a graph export command is shown as recovered command text only.
Returns: the path written to, invisibly
E.16.10 export_spv_html
Export an SPSS Viewer (.spv) file’s tables and charts as standalone HTML
export_spv_html(path, out = NULL)Unlike .jasp/.omv, a .spv archive carries no rendered view of its own (see the file header) – so, unlike export_jasp_html() and export_omv_html(), this does not re-export an existing document. It builds a new HTML page from what import_spv() already decodes: one heading per analysis (its recovered SPSS syntax shown underneath, when recoverable), one <table> per result table, and – for a chart entry (import_spv()’s is_chart = TRUE) – a scatter plot rendered from its own decoded (x, y) points with any fitted trend lines SPSS itself computed drawn over it (see .spv_decode_chart()), embedded as a base64 PNG the same way export_jasp_html() inlines its own plots.
Returns: the path written to, invisibly
E.16.11 read_stat_tables
Read the statistical result tables from a JASP, jamovi or notebook file
read_stat_tables(path)Opens a .jasp, .omv, .spv or .ipynb file and returns every result table as a tidy data frame together with the analysis it belongs to. No statistics knowledge is applied here — this is the lossless extraction layer; semantic typing happens downstream in stat_output_json() / stat_results_long().
Returns: a list with one element per result table, each a list of analysis (the nearest heading above the table), title (the table’s own caption / first heading, when distinguishable), data (a data.frame of the table as rendered, header row as column names), and table_index (1-based ordinal position of this table among all tables in the rendered document — there is no source line to attach, since a JASP/jamovi analysis is GUI-produced). Empty list when the archive has no index.html or no tables.
E.16.12 read_r_output
Extract statistical results from captured R console output
read_r_output(text, source_label = NA_character_, code_lines = NULL)Parses the text an R script prints (as captured from source(script, echo = TRUE)) into tidy result tables, one per detected result block. Handles both one-line test output (t.test, cor.test, …) and fixed-width text tables (summary(lm), aov, anova). The result matches the shape of read_stat_tables(), so it flows into the same STATO typing and ISA-JSON export.
Returns: a list with one element per detected result block, each a list of analysis (the test/section label), title, data (a tidy data.frame), line (1-based source line, or NA when code_lines was not supplied), line_seq (1-based counter of results sharing that line, or NA), and call_fn (the recognised statistical function that produced the block, e.g. "shapiro.test", or "") – the same structure read_stat_tables() returns, plus these three fields. call_fn lets stato_type_column() resolve statistic letters that are ambiguous in the printed output alone.
E.16.13 stat_output_json
Serialise extracted result tables as a structured statistical-output document
stat_output_json(tables, paper_id = "metacheck", source_file = NA_character_)Builds a complete statistical-output document from the result tables of ONE .jasp/.omv file or executed R script: one document per source file, with an analyses array (one element per analysis heading/table) each carrying a results array (one element per result row) of STATO-typed statistic values (via stato_type_column()). This is a metacheck-native schema — a flat, self-describing document in the character of OSD/DDI-Lifecycle/Psych-DS — not a repurposing of an unrelated external standard’s vocabulary; there is no external schema this validates against.
Returns: a list ready to serialise with jsonlite::toJSON(auto_unbox = TRUE), with elements schema, schema_version, paper_id, source_file, source_format, and analyses (a list of list(analysis, results), where each result is list(result_id, row_label, values) and values is a named list keyed by the statistic’s own short name, e.g. t/df/p/d, each a list(value, stato_label, stato_iri) — stato_label/stato_iri omitted when stato_type_column() found no class for that statistic). NULL when there are no tables or no table yields any typed value. result_id identifies the CODE ROW that produced it — see stat_results_long() for the id format (shared between both serialisations of one extraction).
E.16.14 stat_output_validate
Validate a statistical-output document’s native shape
stat_output_validate(doc)Checks that a stat_output_json() document has the required top-level fields, that analyses is a list of list(analysis, results), and that every result carries a result_id and a non-empty values object whose entries each have a value. This is a native structural check, not an executed external JSON Schema — there is no external standard this document conforms to, by design (see the file header comment). Mirrors behaverse_validate()’s and psychds-validate.R’s hand-rolled approach.
Returns: a list with valid (TRUE when no issues), issues (a character vector of problem descriptions), and summary (n_errors, n_analyses, n_results).
E.16.15 stat_output_write
Write extracted statistical output to a dedicated folder
stat_output_write(stat_output, root)Writes reproducibility_check’s accumulated stat_output (one element per source file: JASP/jamovi files via read_stat_tables(), executed R scripts via read_r_output()) to <root>/statistical_output/, a folder sibling to the materialised data/ — so the extracted statistics sit alongside the data and code that produced them. Two views are written from the SAME extraction: one combined results_long.csv (every source file’s stat_results_long() rows stacked, one row per extracted statistic, easiest to load and filter), and one <codename>.statistical_output.json per source file (its stat_output_json() document). Called from inside reproducibility_check itself (not from the later psychds/scienceverse conversion step), so the folder exists at the same point in the pipeline the data/code files do.
Returns: the path to statistical_output/, invisibly, or NULL (nothing written) when stat_output is empty or carries no rows/documents.
E.16.16 stat_results_long
Flatten extracted result tables into one queryable long data frame
stat_results_long(
tables,
paper_id = NA_character_,
source_file = NA_character_
)Turns the nested output of read_stat_tables() into a tidy long table: one row per (analysis, table, result-row, statistic) cell, carrying the STATO typing. This is the form stored in the scienceverse SQLite database, and the basis for later matching of extracted results against the manuscript.
Returns: a data.frame with columns paper_id, source_file, result_id, analysis, table_title, row_label, statistic, stato_label, stato_iri, and value. result_id identifies ONE CELL — one statistic of one code row — sanitised to lower-case letters/digits/underscores: <source_file>_l<line>_<line_seq>_r<row>_<statistic> for an executed R script (from read_r_output()’s echo-based line attribution), or <source_file>_t<table_index>_r<row>_<statistic> for a JASP/jamovi extraction (no source line exists there, so the table’s ordinal position in the rendered document stands in for one); row is the 1-based position of this result within its table, and statistic is that cell’s own column header, sanitised the same way and disambiguated with a trailing counter on the rare repeat within one row (a coefficients matrix with a duplicated header). Every ROW’s shared prefix (everything before the trailing _<statistic>) is .stat_test_id()’s locator, so cells from the same row share that prefix — only the final segment tells them apart. Empty frame (same columns) when there is nothing to flatten.
E.16.17 stato_type_column
Type a result-table column by its header, with a guaranteed fallback
stato_type_column(header, call_fn = NULL)Maps a JASP/jamovi/R result-table column header to a STATO ontology class when one exists, then to a metacheck-minted statistic term for the standard statistics STATO has no class for, and otherwise falls back to the header text itself as an untyped label. Every column therefore gets a type, so no column is ever dropped from the statistical-output export.
Returns: a list with annotationValue (the canonical label, or the header text when unmapped), termSource ("STATO", "metacheck", or ""), and termAccession (the term IRI, or "").
E.16.18 match_reported_output
Match reported statistical tests against the extracted analysis output
match_reported_output(
paper,
output,
include_tables = FALSE,
min_components = 1L
)Recomposes the statistics a paper REPORTS (from paper$eq, grouped by the grp_id that ties one reported test’s numbers together) into whole tests, then checks whether each test’s component values co-occur in a single analysis of the paper’s extracted OUTPUT (reproducibility_check’s statistical output). A test is matched on its whole signature, not lone numbers, so a match means the reported result actually appears in the reproducible output rather than a value coinciding by chance. The result carries, per matched test, which output file and analysis produced it (provenance).
Returns: a data.frame, one row per recomposed reported test, with: text_id, grp_id, reported (the recomposed test as text, e.g. “W=183.5 p=.791 rb=-0.16”), n_components, n_matched (components found in the best-matching output analysis), found (logical), match_values (the matched components as “name=value” pairs), not_matched (the UNMATCHED components of that same test, same “name=value” form – always populated alongside match_values for a partial match, so exactly which claim failed to reproduce is visible, not just a count), source_file / analysis (provenance of the match), confidence (“full” all components matched / “partial” / “none”), and plausible_split – NA unless this test’s grouping came from .regroup_by_evidence() re-deriving test boundaries from OUTPUT co-occurrence rather than trusting extract_tests()’s text-only guess (see that function’s own header comment): TRUE when a component split away from its original reported neighbours landed at a site sharing a row_label token with theirs (the same underlying variable, reached via a different analysis – plausible), FALSE when no such link was found (the split is NOT hidden or downgraded, only flagged, so a reader can judge the match’s plausibility from the source_file/analysis of every piece involved). Attribute "summary" holds the roll-up.
E.17 ORCID and contributor info
E.17.1 check_orcid
Check validity of ORCiD
check_orcid(orcid)Returns: a formatted 16-character ORCiD or FALSE
E.17.2 get_orcid
Get ORCiD from Name
get_orcid(family, given = "*")Returns: A vector of matching ORCiDs
E.17.3 orcid_person
Get Person Details for ORCiD
orcid_person(orcid)Returns: A data frame of details
E.17.4 credit_roles
CRediT Roles
credit_roles(display = c("explain", "names", "abbr"))Returns: list of roles
E.18 File types
E.18.1 filetype
Get file Type from Extension
filetype(filename)Returns: a named vector of file types
E.18.2 file_category
Categorise files
file_category(contents)Returns: the table with new column file_category
E.18.3 txt_classify_content
Classify a downloaded .txt file from its content
txt_classify_content(path)A .txt extension says nothing about what a file holds: research repositories ship experiment data (E-Prime exports, Ibex/task logs), codebooks, and plain prose notes all as .txt. The name-based classifier (data_classify_files()) runs on remote file names before download, so it cannot tell these apart and settles on a conservative guess. Once the file is on disk, its content can. This is the same fetch-then-reclassify pattern the module already uses for zips.
Returns: a single data_check type, or NA_character_ when undecided.
E.18.4 check_file_naming
Check repository files against the FAIR file-naming conventions
check_file_naming(file_name, file_path = file_name, data_type = NA_character_)Checks every file’s name against the naming conventions from the metacheck “machine readable FAIR data and code” guide: no spaces, special characters or diacritics, an actually-classifiable name (not data_type == "unknown"), zero-padded numbering across sibling files, and four path-length budgets (255/228/100/50 characters).
Returns: a data.frame with one row per naming violation: file_name, rule, severity ("bad" or "suggestion" — see Details), detail. 0 rows when every file is clean.
E.18.5 zip_peek
Peek at the contents of a remote ZIP file without downloading it
zip_peek(
url,
tail_bytes = 131072,
cache = FALSE,
skip_on_api_limit = FALSE,
size = NULL
)Uses an HTTP range request to fetch only the tail of a ZIP archive and read its central directory, returning the name and uncompressed size of every entry inside. This lets a downloader decide whether a zip is worth fetching (does it contain data, or only stimuli/assets?) before spending the bandwidth.
Returns: a data.frame with columns name (entry path inside the zip) and size (uncompressed bytes), excluding directory entries; or NULL when the host does not support range requests or the listing cannot be read (the caller should then download-and-inspect). Also returns method, csize, offset, crc and name_bytes, which describe where each member’s bytes sit in the archive so a single member can be fetched without the rest. Names without the zip’s UTF-8 flag are read as CP437 (old Windows zip tools) and returned in UTF-8; name_bytes is the name’s stored length. Zip64 archives (over 4 GB or over 65,535 entries) are read in full, including the sizes and offsets stored only in their Zip64 extra fields; csize and offset are NA only for an entry whose listing is malformed.
E.18.6 zip_decision
Decide whether a remote ZIP is worth downloading for a data archive
zip_decision(
url,
skip_types = "materials",
cache = FALSE,
skip_on_api_limit = FALSE,
size = NULL
)Peeks inside the zip (see zip_peek()) and classifies its entries. A zip is worth downloading if it contains actual data or a codebook; a zip of only stimuli/software is better linked than mirrored.
Returns: a list with worth (TRUE/FALSE, or NA when the peek failed so the caller can fall back to downloading), reason, n_entries, types (table of inner types), and contents (the peeked data.frame or NULL).
E.18.7 zip_peek_cache_clear
Clear the on-disk zip-peek cache
zip_peek_cache_clear()Deletes all cached zip listings (see zip_peek()’s cache argument). Safe at any time – a cleared entry is simply re-peeked over HTTP the next time it is needed.
Returns: the number of cache entries removed, invisibly
E.19 Validation utilities
E.19.1 validate
Validate a module’s output against coded ground truth
validate(gt, module, compare = "table")Runs module on a set of texts you have already coded by hand, then checks its output against your coding. Each text in gt becomes its own test paper; the module’s output table (compare) is joined back to gt by paper_id and text, and for every column that appears in both, a <column>.valid logical column is added marking where the module’s value matches your ground truth. Pass the result to accuracy() to summarise agreement (e.g. sensitivity, specificity, d-prime).
Returns: gt full-joined with the module’s compare table (columns suffixed .gt and .mod where names collide), plus one <column>.valid logical column per compared column
E.19.2 accuracy
Accuracy
accuracy(expected, observed)Signal detection values for modules that classify papers as having a feature or not
Returns: a list of accuracy parameters
E.19.3 cap_gate_count
Build a “cannot process this many items” count-cap message
cap_gate_count(
n_needed,
param,
current,
unit = "item",
context = NULL,
action = "process"
)When a unit of work (a repository, a codebook file, a survey) has more items than a count cap allows, the whole unit is skipped rather than truncated. Returns NULL when within the cap.
Returns: a single string, or NULL when n_needed <= current
E.20 General utilities
E.20.1 api_key_status
Check the API keys metacheck uses
api_key_status()Lists the services for which metacheck uses an API key, whether a key is set, and whether one was recently refused or reached its limit. On a shared server (the metacheck apps and API) the keys are the server’s own; the apps show the message to visitors so they can enter their own key.
Returns: a data frame with one row per service: service, label, key ("user", "server" or "none", see api_keys_use()), problem (NA, "rejected" or "limit"), until (when a limit ends), url (where a key is made) and message (NA without a problem)
E.20.2 api_keys_use
Use your own API keys
api_keys_use(keys = list(), shared = FALSE)Sets keys for the rest of the R session, in place of the keys set as environment variables. The metacheck apps and API call this inside each visitor’s own job with the keys the visitor entered, so the keys are used for that job only.
Returns: invisibly, the previous values of the options that were set, to restore them with options()
E.20.3 email
Set or get email
email(email = NULL)Get or set the contact email metacheck sends with requests to external APIs (e.g. Crossref’s polite pool), so a host that needs to reach you about your usage can do so. Call with no argument to read the current value; call with a valid email address to set it for the rest of the session. Defaults to "metacheck@scienceverse.org" when never set.
Returns: the current option value (character)
E.20.4 online
Check if the host of a URL is online
online(url = "google.com", tries = 3, wait = 1)Resolves the URL’s host via DNS lookup to check whether it is reachable. A scheme (http:///https://) is added automatically if url doesn’t have one. This only confirms the host resolves, not that the specific page or API endpoint responds.
Returns: boolean
E.20.5 verbose
Set or get verbosity
verbose(verbose = NULL)Get or set whether metacheck’s own functions (e.g. message(), pb()) print progress messages and progress bars. Call with no argument to read the current value; call with TRUE/FALSE to set it for the rest of the session. Defaults to TRUE when never set.
Returns: the current option value (logical)
E.20.6 pb
Progress Bar
pb(total, format = "[:bar] :current/:total")Creates a console progress bar (via the progress package) that respects verbose(): when verbosity is off, it returns a dummy object with no-op tick()/message()/terminate() methods, so callers can always call those methods without checking verbose() themselves.
Returns: a function
E.20.7 rep_if
Replace If
rep_if(x, y, replace = NULL)Replace values if NULL, NA, or specified value
Returns: x, with each NULL element and each value in replace replaced by the matching value of y
E.20.8 path_sanitize
Sanitize File Path
path_sanitize(
path,
replacement = "_",
remove_whitespace = TRUE,
keep_sep = TRUE
)Make sure user-input file names are not problematic.
Returns: the sanitized vector
E.20.9 message
Less scary green messages
message(..., domain = NULL, appendLF = TRUE)Metacheck’s replacement for base::message(): prints in green when run interactively (so it reads as informational rather than a warning), and is silent entirely when verbose() is FALSE.
Returns: TRUE
E.20.10 logger
Log messages
logger(label = "", contents = list(), logpath = NULL)Adds a logging message to the log. Keeps the log as a maximum of 1000 rows.
Returns: called for side effects of writing to log, returns logpath
E.20.11 logpath
Get log path
logpath()Returns the log file of this R session, and creates it if missing. The log is kept in the session’s temporary folder, which R removes when the session ends: CRAN does not allow a package to keep files elsewhere unless the user asks for it.
Returns: the log file path
E.20.12 lastlog
Get the last log
lastlog(i = 1, logpath = NULL)Reads entries back from the on-disk log written by logger() (newest first, so i = 1 is the most recent entry). Returns a single entry as a list, or several entries row-bound into a data frame.
Returns: a list of the last log item, or a data frame of multiple items
%empty_or%() — (no documentation found)