Package 'metacheck'

Description

Signal detection values for modules that classify papers as having a feature or not

Usage

accuracy(expected, observed)

Arguments

Value

a list of accuracy parameters

Description

Match table from bib table

Usage

add_bib_match(paper, min_score = 50)

Arguments

Value

the paper or paperlist with bib_match table added

Examples

## Not run: 
paper <- demopaper()
paper$bib_match <- NULL # remove existing
paper2 <- add_bib_match(paper)
paper2$bib_match

## End(Not run)

Description

Retrieve info from AsPredicted by URL

Usage

aspredicted_info(ap_url, id_col = 1, wait = 1)

Arguments

Value

a data frame of information

Description

Find AsPredicted Links in Papers

Usage

aspredicted_links(paper)

Arguments

Value

a table with the AsPredicted url in the first (href) column

Examples

aspredicted_links(psychsci)

Description

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.

Usage

causal_relations(
  sentence,
  rel_mode = "auto",
  rel_threshold = 0.5,
  cause_decision = "cls+span",
  timeout = 10,
  verbose = FALSE
)

Arguments

Details

The function uses Gradio’s two-step queue API: (1) a POST request enqueues the job and returns an event_id; (2) a GET request streams text/event-stream frames until event: complete. Many Gradio apps emit a double-encoded completion payload of the form ⁠["<final JSON string>"]⁠. This function unwraps that to obtain the final JSON structure (an array of items containing causal and relations) before parsing.

If a sentence has no relations, the output includes one row with cause = NA, effect = NA, and the sentence’s causal flag (as returned by the model). For sentences with multiple relations, the function returns one row per relation.

Value

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 (or NA),

• effect (character): extracted effect span (or NA).

References

Norouzi, R., Kleinberg, B., Vermunt, J. K., & van Lissa, C. J. (2025). Capturing causal claims: A fine-tuned text mining model for extracting causal sentences from social science papers. Research Synthesis Methods, 16(1), 139–156. https://doi.org/10.1017/rsm.2024.13

Hugging Face Model Card: rasoultilburg/SocioCausaNet https://huggingface.co/rasoultilburg/SocioCausaNet

Examples

## Not run: 
# Single sentence
df1 <- causal_relations("Smoking causes cancer")
print(df1)

# Multiple sentences (batch)
df2 <- causal_relations(c("Insomnia causes depression.", "Rain leads to flooding."))
print(df2)

# Custom parameters and verbose diagnostics
df3 <- causal_relations(
  sentence = "Stress increases blood pressure.",
  rel_mode = "auto",
  rel_threshold = 0.4,
  cause_decision = "cls+span",
  timeout = 10,
  verbose = TRUE
)
print(df3)

## End(Not run)

Description

Check code for the presence of absolute paths

Usage

code_abs_path(code_text)

Arguments

Value

a vector of absolute paths

Examples

code_text <- c(
  "file <- 'C:/User/lakens/file.R'",
  "tmp <- '/User/lakens/file.html'",
  "convert(file, tmp)"
)
code_abs_path(code_text)

Description

Convert Rmd/qmd files to R code only

Usage

code_extract_r(
  file_path = NULL,
  save_path = NULL,
  documentation = 0,
  text = NULL
)

Arguments

Value

a character vector

Examples

file_path <- demofile("qmd")
code_text <- code_extract_r(file_path)

Description

Get files referenced in code

Usage

code_file_refs(code_text, lang = c("R", "SPSS", "SAS", "Stata"))

Arguments

Value

a vector of files that are referenced in the code

Examples

code_text <- c(
  'source("functions.R")',
  'a <- "bread"',
  'b <- read.csv("file.csv")'
)
code_file_refs(code_text, "R")

Description

Detects code language used in files, only for languages metacheck currently processes (R, SAS, SPSS, Stata).

Usage

code_lang(file_name)

Arguments

Value

a vector of languages

Examples

file_name <- "file.R"
code_lang(file_name)

file_name <- c("file.Rmd", "file.SAS", "file.r", "file.qmd", "file.txt")
code_lang(file_name)

Description

Returns the lines on which library/require calls exist. This is a helper function for the code_check module.

Usage

code_library_lines(code_text, lang = c("R", "SPSS", "SAS", "Stata"))

Arguments

Value

a data frame with columns code and line (the line numbers on which library calls exist, after removing blank lines and comments)

Examples

code_text <- c(
  "library(dplyr)",
  "",
  "# this line won't count",
  "library(tidyr)",
  "renv::install('metacheck')"
)
code_library_lines(code_text, "R")

Description

Get Code Composition Stats

Usage

code_line_stats(code_text, lang = c("R", "SPSS", "SAS", "Stata"))

Arguments

Value

list with items total_lines, comment_lines, code_lines, and percent_comment

Examples

code_text <- c(
  "library(dplyr)",
  "",
  "# this line is a comment",
  "a <- 1"
)
code_line_stats(code_text, "R")

Description

Parse code to check for errors

Usage

code_parse_r(file_path = "", text = NULL)

Arguments

Value

a data frame with columns file_path and line

Examples

file_path <- demofile("qmd")
code_parse_r(file_path)

Description

Read code from files

Usage

code_read(file_path)

Arguments

Value

a character vector of the file contents

Examples

file_path <- demofile("json")
text <- code_read(file_path)

Description

Remove comments from code text

Usage

code_remove_comments(code_text, lang = c("R", "SPSS", "SAS", "Stata"))

Arguments

Value

the code_text minus comment lines

Examples

code_text <- c(
  "# this is a comment",
  "",
  "x <- 'And this is code'"
)
code_text_nc <- code_remove_comments(code_text, "R")

Description

A helper function for making module reports.

Usage

collapse_section(
  text,
  title = "Learn More",
  callout = c("tip", "note", "warning", "important", "caution"),
  collapse = TRUE
)

Arguments

Value

text

Examples

text <- c("Paragraph 1...", "Paragraph 2...")
collapse_section(text) |> cat()

Description

Uses grobid or bibr to convert a file to paper format.

Usage

convert(
  file_path,
  save_path = ".",
  method = c("auto", "bibr", "grobid", "xml"),
  crossref_lookup = FALSE,
  keep_xml = TRUE,
  ...
)

Arguments

Details

Both bibr and grobid can handle PDF files. Only bibr can convert doc or docx files. Already-converted grobid XML files can be converted to bibr format (set crossref_lookup=TRUE to add a bib_match table). If the file_path is a directory, the method will be xml if any XML files are present, and bibr if only doc or docx files are present.

Value

the path to the JSON file

Description

Converts document files (PDF, DOC, DOCX) to structured JSON using the bibr extraction service. Supports two backends: the Scienceverse platform ("scivrs") which uses a job queue with load balancing, and a self-hosted bibr instance ("selfhosted") for direct API access.

Usage

convert_bibr(
  file_path,
  save_path = ".",
  backend = c("auto", "scivrs", "selfhosted"),
  api_key = NULL,
  api_url = NULL,
  include_figures = FALSE,
  start_page = 1,
  end_page = Inf,
  poll_interval = 2,
  timeout = 600
)

Arguments

Details

When backend = "auto" (the default), the "scivrs" backend is used if api_key is provided or the SCIVRS_API_KEY environment variable is set. Otherwise, "selfhosted" is used (no authentication required).

Value

Path(s) to the saved JSON file(s)

Examples

## Not run: 
# Auto-detect backend from environment variables
pdf <- demofile("pdf")
convert_bibr(pdf)

# Explicitly use Scienceverse platform
convert_bibr(pdf, backend = "scivrs")

# Use self-hosted bibr instance
convert_bibr(pdf, backend = "selfhosted")

# Extract specific pages
convert_bibr(pdf, start_page = 1, end_page = 10)

# Directory of papers
dir <- system.file("demo", package = "metacheck")
convert_bibr(dir, save_path = "results/")

## End(Not run)

Description

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).

Usage

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
)

Arguments

Details

Consolidation of citations, headers, and funders looks up these items in CrossRef or another database to fix or enhance information (see https://grobid.readthedocs.io/en/latest/Consolidation/). This can slow down conversion. Consolidating headers is only useful for published papers, and can be set to 0 for work in prep. We recommend you leave these defaults at 0 and use crossref_loookup = TRUE when converting from grobid XML to bibr JSON format with the convert() function.

Value

XML object

Description

Valid selects for crossref API are:

Usage

crossref_doi(
  doi,
  select = c("DOI", "type", "title", "author", "container-title", "volume", "issue",
    "page", "URL", "abstract", "year", "error")
)

Arguments

Details

abstract, URL, resource, member, posted, score, created, degree, update-policy, short-title, license, ISSN, container-title, issued, update-to, issue, prefix, approved, indexed, article-number, clinical-trial-number, accepted, author, group-title, DOI, is-referenced-by-count, updated-by, event, chair, standards-body, original-title, funder, translator, published, archive, published-print, alternative-id, subject, subtitle, published-online, publisher-location, content-domain, reference, title, link, type, publisher, volume, references-count, ISBN, issn-type, assertion, deposited, page, content-created, short-container-title, relation, editor

Value

data frame with DOIs and info

Examples

doi <- "10.7717/peerj.4375"
## Not run: 
# cr_info <- crossref_doi(doi)

## End(Not run)

Description

Look up Reference in CrossRef

Usage

crossref_query(
  ref,
  min_score = 50,
  rows = 1,
  select = c("DOI", "score", "type", "title", "author", "editor", "publisher",
    "container-title", "year", "volume", "issue", "page", "URL")
)

Arguments

Details

The argument ref can take many formats. Crossref queries only look for authors, title, and container-title (e.g., journal or book), but extra information doesn't seem to hurt.

• a text reference or fragment

• a bibentry object (authors, title and container will be extracted)

• a vector of text or bibentry objects

• a paper object (the bib table will be extracted)

Valid selects for this route are: abstract, URL, resource, member, posted, score, created, degree, update-policy, short-title, license, ISSN, container-title, issued, update-to, issue, prefix, approved, indexed, article-number, clinical-trial-number, accepted, author, group-title, DOI, is-referenced-by-count, updated-by, event, chair, standards-body, original-title, funder, translator, published, archive, published-print, alternative-id, subject, subtitle, published-online, publisher-location, content-domain, reference, title, link, type, publisher, volume, references-count, ISBN, issn-type, assertion, deposited, page, content-created, short-container-title, relation, editor

Value

doi

Examples

ref <- paste(
  "Lakens, D., Mesquida, C., Rasti, S., & Ditroilo, M. (2024).",
  "The benefits of preregistration and Registered Reports.",
  "Evidence-Based Toxicology, 2(1)."
)
## Not run: 
cr <- crossref_query(ref)

## End(Not run)

Description

Doi.org Info from DataCite

Usage

datacite_doi(doi)

Arguments

Value

bib_match data frame

Examples

doi <- "10.5281/zenodo.2669586"
## Not run: 
doi_info <- datacite_doi(doi)

## End(Not run)

Description

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.

Usage

demofile(ext = c("json", "pdf", "docx", "doc", "xml", "qmd"))

Arguments

Value

file path

Examples

json <- demofile()
pdf <- demofile("pdf")

Description

Get demo paper

Usage

demopaper()

Value

paper object

Examples

paper <- demopaper()

Description

Clean DOIs

Usage

doi_clean(doi)

Arguments

Value

a character vector of cleaned DOIs (no https://doi.org or DOI:)

Examples

doi_clean("https://doi.org/10.1038/nphys1170")
doi_clean("doi:10.1038/nphys1170")
doi_clean("DOI: 10.1038/nphys1170")

Description

Doi.org Info from DOI

Usage

doi_lookup(doi)

Arguments

Value

data frame with DOIs and info

Examples

doi <- "10.7717/peerj.4375"
## Not run: 
doi_info <- doi_lookup(doi)

## End(Not run)

Description

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.

Usage

doi_resolves(doi, timeout = 10)

Arguments

Value

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.

Examples

## Not run: 
doi_resolves("10.1038/nphys1170") # Expected: TRUE
doi_resolves("10.1234/invalid.doi") # Expected: FALSE

## End(Not run)

Description

Validate DOI format

Usage

doi_valid_format(doi)

Arguments

Value

a logical vector

Examples

doi_valid_format("10.1038/nphys1170")
doi_valid_format("no.no.10.1038")

Description

Set or get email

Usage

email(email = NULL)

Arguments

Value

the current option value (character)

Examples

email()

Description

Useful emojis

Usage

emojis

Format

An object of class list of length 32.

Details

• General: "check", "star", warning", "stop", "x", "no", "thumbs_up", "thumbs_down", "info", "question"

• Traffic Lights: "tl_green", "tl_yellow", "tl_red", "tl_info", "tl_na", "tl_fail"

• Hearts: "red", "orange", "yellow", "green", "blue", "purple", "brown", "black", "white", "pink"

Description

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.

Usage

extract_eq(paper)

Arguments

Details

This will catch most comparators like =<>~and most versions of scientific notation like 5.0 x 10^-2 or 5.0e-2. If you find any formats that are not correctly handled by this function, please contact the author.

Value

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.

Examples

paper <- demopaper()
equations <- extract_eq(paper)

Description

List all p-values in the text, returning the matched text (e.g., 'p = 0.04') and document location in a table.

Usage

extract_p_values(paper)

Arguments

Details

Note that this will not catch p-values reported like "the p-value is 0.03" because that results in a ton of false positives when papers discuss p-value thresholds. If you need to detect text like that, use the text_search() function and a custom pattern.

This will catch most comparators like =<>~ and most versions of scientific notation like 5.0 x 10^-2 or 5.0e-2. If you find any formats that are not correctly handled by this function, please contact the author.

Value

a table

Examples

paper <- demopaper()
p_values <- extract_p_values(paper)

Description

Get a table of URLs from a paper or paperlist. Matches urls that start with http or doi:

Usage

extract_urls(paper)

Arguments

Value

a table

Examples

paper <- demopaper()
urls <- extract_urls(paper)

Description

View a figure image

Usage

fig_image_view(paper, figure_id = 1)

Arguments

Value

plots the figure

Examples

paper <- demopaper()
fig_image_view(paper, 1)
fig_image_view(paper, 2)

Description

Categorise files

Usage

file_category(contents)

Arguments

Value

the table with new column file_category

Examples

contents <- c("script.R", "data.csv", "README", "codebook.csv")
file_category(contents)

Description

Get file Type from Extension

Usage

filetype(filename)

Arguments

Value

a named vector of file types

Examples

filetype("script.R")

Description

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.

Usage

FLoRA()

Format

A data frame with 8 columns:

doi_o

DOI of original study

apa_ref_o

APA reference of original study

doi_r

DOI of replication study (may be NA if url_r is provided)

apa_ref_r

APA reference of replication study

url_r

URL of replication study (used when DOI is not available)

outcome

replication outcome

outcome_quote

quote describing replication outcome

type

replication or reproduction

Value

a data frame

Source

https://osf.io/9r62x/files/t4j8f

Examples

FLoRA()

Description

Get date FLoRA was updated

Usage

FLoRA_date()

Value

the date

Examples

FLoRA_date()

Description

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.

Usage

FLoRA_update()

Value

the path to the data frame (invisibly)

Description

Formats a structured author list (data frame with given/family columns) as a display string.

Usage

format_bib_authors(authors)

Arguments

Value

a character string (or vector) of formatted author names

Examples

authors <- data.frame(given = c("Alice H.", "Wendy"),
                      family = c("Eagly", "Wood"))
format_bib_authors(authors)

Description

Format a reference for display in a report.

Usage

format_ref(bib)

Arguments

Details

The argument bib should be a bibentry object (e.g., like those made by citation(), but it can also handle a bibtex object or a bibtex formatted character vector. If these do not read in as valid bibtex, the original text of bib will be returned unformatted.

Value

formatted text

Examples

mc <- citation("metacheck")
format_ref(mc)

# handles bibtext
bib_mc <- utils::toBibtex(mc)
format_ref(bib_mc)

paper <- demopaper()
format_ref(paper$bib$ref[1:2])

Description

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.

Usage

get_prev_outputs(module, item, parent_n = 2)

Arguments

Value

the extracted list item, or NULL if not found

Examples

# .__mc__prev_outputs is usually created by `module_run()`
.__mc__prev_outputs <- list(mod_1 = list(a = 1, b = 2))
f <- function(item) {
  get_prev_outputs("mod_1", item)
}
f("a")
f("d")

Description

Get File List from GitHub

Usage

github_files(repo, dir = "", recursive = FALSE)

Arguments

Value

a data frame of files

Examples

## Not run: 
github_files("scienceverse/metacheck")

## End(Not run)

Description

Get GitHub Repo Info

Usage

github_info(repo, recursive = FALSE)

Arguments

Value

a list of information about the repo

Examples

## Not run: 
github_info("scienceverse/metacheck")

## End(Not run)

Description

Get Languages from GitHub Repo

Usage

github_languages(repo)

Arguments

Value

vector of languages

Examples

## Not run: 
github_languages("scienceverse/metacheck")

## End(Not run)

Description

GitHub links can be in PDFs in several ways.

Usage

github_links(paper)

Arguments

Value

a table with the GitHub url in the first (text) column

Examples

github_links(psychsci)

Description

Get README from GitHub

Usage

github_readme(repo)

Arguments

Value

a character string of the README contents

Examples

## Not run: 
github_readme("scienceverse/metacheck")

## End(Not run)

Description

Get Short GitHub Repo Name

Usage

github_repo(repo)

Arguments

Value

character string of short repo name

Examples

github_repo("scienceverse/metacheck")
github_repo("https://github.com/scienceverse/metacheck/")
github_repo("https://github.com/scienceverse/metacheck.git")

Description

Convert Grobid TEI XML file to bibr format

Usage

grobid_to_bibr(xml_path, save_path = ".", crossref_lookup = FALSE)

Arguments

Value

a paper object

Description

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.

Usage

json_expand(table, col = "answer", suffix = c("", ".json"))

Arguments

Value

the table plus the expanded columns

Examples

table <- data.frame(
  paper_id = 1:5,
  answer = c(
    '{"number": "1", "letter": "A", "bool": true}',
    '{"number": "2", "letter": "B", "bool": "FALSE"}',
    '{"number": "3", "letter": "", "bool": null}',
    "oh no, the LLM misunderstood",
    '{"number": "5", "letter": ["E", "F"], "bool": false}'
  )
)

expanded <- json_expand(table, "answer")
expanded

Description

Get the last log

Usage

lastlog(i = 1, logpath = NULL)

Arguments

Value

a list of the last log item, or a data frame of multiple items

Examples

# set up 2 log items
logger("test", list(msg = "hi"))
logger("test", list(msg = "hi again"))

lastlog()
lastlog(2)
lastlog(1:2)

Description

Make an html link

Usage

link(url, text = url, new_window = TRUE, type = "")

Arguments

Value

string

Examples

link("https://scienceverse.org")

Description

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.

Usage

llm(
  text,
  system_prompt,
  type = NULL,
  text_col = "text",
  model = llm_model(),
  params = list()
)

Arguments

Details

You will need to get your own API key from https://console.groq.com/keys. To avoid having to type it out, add it to the .Renviron file in the following format (you can use usethis::edit_r_environ() to access the .Renviron file)

GROQ_API_KEY="key_value_asdf"

See https://console.groq.com/docs for more information

Value

a data frame of results

Examples

## Not run: 
# Free-text query
text <- c("hello", "number", "ten", 12)
system_prompt <- "Is this a number? Answer only 'TRUE' or 'FALSE'"
is_number <- llm(text, system_prompt)

# Structured extraction
type_spec <- ellmer::type_object(
  is_number = ellmer::type_boolean("Whether the input is a number")
)
result <- llm(c("hello", "42"), "Classify the input.", type = type_spec)

## End(Not run)

Description

Set the maximum number of calls to the LLM

Usage

llm_max_calls(n = NULL)

Arguments

Description

Use llm_model_list() to get a list of available models

Usage

llm_model(model = NULL)

Arguments

Description

List available LLM models for the specified platform.

Usage

llm_model_list(platform = NULL)

Arguments

Details

For platforms other than groq, returns the value from the corresponding ellmer::models_platform function.

Value

a data frame of models and info

Examples

## Not run: 
llm_model_list()

## End(Not run)

Description

Mainly for use in optional LLM workflows in modules

Usage

llm_use(llm_use = NULL)

Arguments

Value

the current option value (logical)

Examples

if (llm_use()) {
  print("We can use LLMs")
} else {
  print("We will not use LLMs")
}

Description

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.

Usage

local_files(path, recursive = FALSE)

Arguments

Value

a data frame with columns repo_url, file_name, file_url, file_location, file_size, file_type

Examples

## Not run: 
  local_files("my_project")

## End(Not run)

Description

Adds a logging message to the log. Keeps the log as a maximum of 1000 rows.

Usage

logger(label = "", contents = list(), logpath = NULL)

Arguments

Value

called for side effects of writing to log, returns logpath

Examples

logpath <- tempfile(fileext = ".log")
logger("test", list(x = 1), logpath)
lastlog()

Description

See the help files for a module by name (get a list of names from module_list())

Usage

module_help(module = NULL)

Arguments

Value

the help text

Examples

module_help("marginal")

Description

Get module information

Usage

module_info(module)

Arguments

Value

a list of module info

Examples

module_info("all_p_values")

Description

List modules

Usage

module_list(module_dir = system.file("modules", package = "metacheck"))

Arguments

Value

a data frame of modules

Examples

mods <- module_list()

Description

Report from module output

Usage

module_report(module_output, header = 3)

Arguments

Value

text

Examples

paper <- demopaper()
op <- module_run(paper, "stat_p_exact")
module_report(op) |> cat()

Description

Run a module

Usage

module_run(paper, module, ...)

Arguments

Value

a list of the returned table and report text

Examples

module_run(psychsci[[1]], "all_p_values")

Description

Create a Module from a Template

Usage

module_template(module_name, path = "./modules")

Arguments

Value

the file path (invisibly)

Description

See details for a list of root-level fields that can be selected.

Usage

openalex_doi(doi, select = NULL)

Arguments

Details

See https://docs.openalex.org/api-entities/works/work-object for explanations of the information you can retrieve about works.

Root-level fields for the select argument:

• id

• doi

• title

• display_name

• publication_year

• publication_date

• ids

• language

• primary_location

• type

• type_crossref

• indexed_in

• open_access

• authorships

• institution_assertions

• countries_distinct_count

• institutions_distinct_count

• corresponding_author_ids

• corresponding_institution_ids

• apc_list

• apc_paid

• fwci

• has_fulltext

• fulltext_origin

• cited_by_count

• citation_normalized_percentile

• cited_by_percentile_year

• biblio

• is_retracted

• is_paratext

• primary_topic

• topics

• keywords

• concepts

• mesh

• locations_count

• locations

• best_oa_location

• sustainable_development_goals

• grants

• datasets

• versions

• referenced_works_count

• referenced_works

• related_works

• abstract_inverted_index

• abstract_inverted_index_v3

• cited_by_api_url

• counts_by_year

• updated_date

• created_date

Value

list with DOIs and info

Examples

doi <- "10.7717/peerj.4375"
## Not run: 
oa_info <- openalex_doi(doi)
oa_info <- openalex_doi(doi, "title")

## End(Not run)

Description

Look up a reference in OpenAlex

Usage

openalex_query(title, source = NA, authors = NA, strict = TRUE)

Arguments

Value

A data frame with citation info

Examples

## Not run: 
openalex_query("Sample Size Justification", "Collabra Psychology")

## End(Not run)

Description

Check the status of the OSF API server.

Usage

osf_api_check(
  osf_api = getOption("metacheck.osf.api"),
  on_error = c("stop", "warn", "ignore")
)

Arguments

Details

The OSF API server is down a lot, so it's often good to check it before you run a bunch of OSF functions. When the server is down, it can take several seconds to return an error, so scripts where you are checking many URLs can take a long time before you realise they aren't working.

You can only make 100 API requests per hour, unless you authorise your requests, when you can make 10K requests per day. The osf functions in metacheck often make several requests per URL to get all of the info. You can authorise them by creating an OSF token at https://osf.io/settings/tokens and including the following line in your .Renviron file:

OSF_PAT="replace-with-your-token-string"

Value

the OSF status

Examples

osf_api_check()

Description

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.

Usage

osf_check_id(osf_id)

Arguments

Value

a vector of valid IDs, with NA in place of invalid IDs

Examples

osf_check_id("pngda")
osf_check_id("osf.io/pngda")
osf_check_id("https://osf.io/pngda")
osf_check_id("https://osf .io/png da") # rogue whitespace
osf_check_id("pnda") # invalid

Description

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.

Usage

osf_delay(delay = NULL)

Arguments

Examples

osf_delay()

Description

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.

Usage

osf_file_download(
  osf_id,
  download_to = ".",
  max_file_size = 10,
  max_download_size = 100,
  max_folder_length = Inf,
  ignore_folder_structure = FALSE,
  pb = NULL
)

Arguments

Details

Some differences may exist because the OSF allows longer file names with characters that may not be allowed on a file system, so these are cleaned up when downloading.

You can limit downloads to only files under a specific size (defaults to 10MB) and only a maximum download size (largest files will be omitted until total size is under the limit). Omitted files will be listed as messages in verbose mode, and included in the returned data frame with the downloaded column value set to FALSE.

Value

data frame of file info

Examples

## Not run: 
osf_file_download("6nt4v")

## End(Not run)

Description

OSF API queries only return up to 10 items per page, so this helper functions checks for extra pages and returns all of them

Usage

osf_get_all_pages(url, page_end = Inf)

Arguments

Value

a table of the returned data

Examples

# get the 20 newest preprints
## Not run: 
osf_api <- getOption("metacheck.osf.api")
url <- sprintf("%s/preprints/?search=date_created-desc", osf_api)
preprints <- osf_get_all_pages(url, 2)

## End(Not run)

Description

Retrieve info from the OSF by ID

Usage

osf_info(osf_url, id_col = 1, recursive = FALSE, pb = NULL)

Arguments

Value

a data frame of information

Examples

## Not run: 
# get info on one OSF node
osf_info("pngda")

# also get child nodes and files
osf_info("https://osf.io/6nt4v", recursive = TRUE)

## End(Not run)

Description

Get all OSF links.

Usage

osf_links(paper)

Arguments

Value

a table with the OSF url in the first (href) column

Examples

osf_links(psychsci)

Description

Get A list of preprints from the OSF

Usage

osf_preprint_list(
  provider = NULL,
  date_created = NULL,
  date_modified = NULL,
  page_start = 1,
  page_end = page_start
)

Arguments

Value

a table of preprint info

Examples

## Not run: 
dc <- c("2025-09-01", "2025-10-01")
pp <- osf_preprint_list("psyarxiv", date_created = dc)
files <- pp$primary_file

## End(Not run)

Description

Get OSF GUID Type

Usage

osf_type(guid)

Arguments

Value

the type

Examples

# osf_type("pngda")

Description

Get Paper IDs

Usage

paper_id(paper)

Arguments

Value

a vector of paper_ids

Examples

paper_id(psychsci)

Description

Return a table from a paper object or concatenate tables across a list of paper objects.

Usage

paper_table(paper, table, cols = NULL)

Arguments

Value

a merged table

Examples

biblio <- paper_table(psychsci[1:10], "bib")
xrefs <- paper_table(psychsci[1:10], "xref")

Description

Checks if a paper object conforms to the JSON schema.

Usage

paper_validate(paper)

Arguments

Value

TRUE or error

Examples

paper <- list(paper_id = "Not a paper object")
tryCatch(
  paper_validate(paper),
  error = \(e) print(e$message)
)

paper <- demopaper()
paper_validate(paper)

Description

Save a paper as a JSON file.

Usage

paper_write(paper, file_name = NULL, save_path = ".")

Arguments

Value

the path to the JSON file

Examples

## Not run: 
paper <- demopaper()
paper$info$title <- "New title"
paper_write(paper, "new_paper")

## End(Not run)

Description

Make sure user-input file names are not problematic.

Usage

path_sanitize(
  path,
  replacement = "_",
  remove_whitespace = TRUE,
  keep_sep = TRUE
)

Arguments

Value

the sanitized vector

Examples

path <- "/My Files/x><y.pdf"
path_sanitize(path)
path_sanitize(path, replacement = "~")
path_sanitize(path, remove_whitespace = FALSE)
path_sanitize(path, keep_sep = FALSE)

Description

Helper function for conditional plurals. For example, if you want to return "1 error" or "2 errors", you can use this in a sprintf().

Usage

plural(n, singular = "", plural = "s")

Arguments

Value

a string

Examples

n <- 0:3
sprintf("I have %d friend%s", n, plural(n))
sprintf("I have %d %s", n, plural(n, "octopus", "octopi"))

Description

250 open access papers from Psychological Science.

Usage

psychsci

Format

A list of 250 paper objects

Source

https://journals.sagepub.com/home/pss

Description

Takes a DOI, and retrieves information from pubpeer related to post-publication peer review comments.

Usage

pubpeer_comments(doi)

Arguments

Value

a dataframe with information from pubpeer

Examples

doi <- c(
  "10.1038/s41598-025-24662-9",
  "10.1177/0146167211398138"
)
pubpeer_comments(doi)

Description

Retrieve files from ResearchBox by URL

Usage

rbox_file_download(rb_url, pb = NULL)

Arguments

Value

a data frame of information

Description

Retrieve info from ResearchBox by URL

Usage

rbox_info(rb_url, id_col = 1, pb = NULL)

Arguments

Value

a data frame of information

Examples

## Not run: 
# get info on one OSF node
rbox_info("https://researchbox.org/801")

## End(Not run)

Description

Find ResearchBox Links in Papers

Usage

rbox_links(paper)

Arguments

Value

a table with the ResearchBox url in the first (href) column

Examples

rbox_links(psychsci)

Description

Read in grobid XML or bibr JSON

Usage

read(file_path, include_images = FALSE, recursive = FALSE)

Arguments

Value

a paper or paperlist

Description

Return a table with fixed DOIs and reference text from a paper object or concatenate tables across a list of paper objects.

Usage

ref_table(paper)

Arguments

Value

a merged table

Examples

biblio <- ref_table(psychsci[[1]])

Description

Run specified modules on a paper and generate a report in quarto (qmd), html, or pdf format.

Usage

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_accuracy", "ref_replication", "ref_retraction", "ref_pubpeer",
    "ref_summary"),
  output_file = paste0(paper$paper_id, "_report.", output_format),
  output_format = c("html", "qmd"),
  args = list()
)

Arguments

Details

Pass arguments to modules in a named list of lists, using the same names as the modules argument. You only need to specify modules with arguments.

args <- list(power = list(seed = 8675309))

Value

the file path the report is saved to

Examples

## Not run: 
paper <- demopaper()
report(paper)

## End(Not run)

Description

Launch the Report app: upload a PDF and generate a report with one click, with privacy options for what is sent to external servers.

Usage

report_app(quiet = FALSE, ...)

Arguments

Value

NULL (invisibly)

Examples

## Not run: 
report_app()

## End(Not run)

Description

Runs modules in order on the paper and orders by section and traffic light.

Usage

report_module_run(paper, modules, args = list())

Arguments

Details

Pass arguments to modules in a named list of lists, using the same names as the modules argument. You only need to specify modules with arguments.

args <- list(power = list(seed = 8675309))

Value

a list of module outputs

Examples

paper <- demopaper()
modules <- c("stat_p_exact", "stat_p_nonsig")
module_output <- report_module_run(paper, modules)

Description

Create Report from Module Output

Usage

report_qmd(module_output, paper = list())

Arguments

Value

report text

Description

A function to display tables in reports.

Usage

report_table(table, colwidths = "auto", maxrows = 2, escape = FALSE)

Arguments

Value

the datatable

Examples

report_table(iris)

Description

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.

Usage

retractionwatch()

rw()

Format

A data frame with 44784+ rows and 2 columns:

doi

Document ID

retractionwatch

Nature of note(s)

Value

a data frame

Source

https://api.labs.crossref.org/data/retractionwatch

Examples

retractionwatch()

Description

Get date retractionwatch was updated

Usage

rw_date()

Value

the date

Examples

rw_date()

Description

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.

Usage

rw_update()

Value

the path to the data frame (invisibly)

Description

A helper function for making module reports.

Usage

scroll_table(
  table,
  colwidths = "auto",
  maxrows = 2,
  escape = FALSE,
  column = "body"
)

Arguments

Details

See quarto article layout for column options. The most common are "body" (centre column), "page" (span all columns"), and "margin" (only in right margin).

To set colwidths, use a numeric or character vector. For a numeric vector, numbers greater than 1 will be interpreted as pixels, less than 1 as percents. Character vectors will be passed as is (e.g., "3em"). If you only want to specify some columns, set the others to NA, like c(200, NA, 200, NA).

Value

the markdown R chunk to create this table

Examples

scroll_table(LETTERS)

Description

Check Stats

Usage

stats(text, ...)

Arguments

Value

a table of statistics

Examples

paper <- demopaper()
stats(paper)

Description

Create a paper object with the specified text (mainly for testing/demos).

Usage

test_paper(text = LETTERS, url = character(0))

Arguments

Value

a paper object

Examples

# to test a paper with a specific URL
p <- test_paper("https://osf.io/abcde")

Description

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").

Usage

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
)

Arguments

Value

a results table with the expanded text

Examples

# single paper search
paper <- demopaper()
res_tbl <- text_search(paper, "p =", return = "match")
expanded <- text_expand(res_tbl, paper)

# multiple paper search
papers <- psychsci
res_tbl <- text_search(papers, "replicate")
expanded <- text_expand(res_tbl, papers, plus = 1, minus = 1)

Description

Search the text of a paper or list of paper objects. Also works on the table results of a text_search() call.

Usage

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
)

Arguments

Details

The section argument can take a vector of section names, or a PERL regular expression (use ".*" to match all sections). Possible section types are abstract, intro, method, results, discussion, references, acknowledgment, funding, endnote, footnote, table, figure, and unknown. The default includes all sections except references, tables and figures.

Value

a data frame of matches

Examples

paper <- demopaper()
all_text <- text_search(paper)
study <- text_search(paper, "study")
equations <- text_search(paper, "\\b\\S+\\s*(=|<)\\s*[0-9\\.]+", return = "match")
no_numbers <- text_search(paper, "\\d", exclude = TRUE)

Description

Validate

Usage

validate(gt, module, compare = "table")

Arguments

Value

something

Examples

validate("p < .05", "stat_p_exact")

Description

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.

Usage

zenodo_file_download(
  zenodo_id,
  download_to = ".",
  max_file_size = 10,
  max_download_size = 100,
  pb = NULL
)

Arguments

Details

You can limit downloads to only files under a specific size (defaults to 10MB) and only a maximum download size (largest files will be omitted until total size is under the limit). Omitted files will be listed as messages in verbose mode, and included in the returned data frame with the downloaded column value set to FALSE.

Value

data frame of file info

Examples

## Not run: 
  zenodo_file_download("2591593")

## End(Not run)

Description

Retrieve info from Zenodo by URL

Usage

zenodo_info(zenodo_url, id_col = 1, pb = NULL)

Arguments

Value

a data frame of information

Examples

## Not run: 
  # get info on one zenodo link
  zenodo_info("https://doi.org/10.5281/zenodo.18648142")

## End(Not run)

Description

Find Zenodo Links in Papers

Usage

zenodo_links(paper)

Arguments

Value

a table with the Zenodo url in the first (text) column

Examples

zenodo_links(psychsci)