Description: DAGs With Omitted Objects Displayed (DAGWOOD) is a framework to help reveal key hidden assumptions in a causal DAG. This package provides an implementation of the DAGWOOD algorithm. Further description can be found in Haber et al (2022) <DOI:10.1016/j.annepidem.2022.01.001>.

Title: Interface with Google Cloud Document AI API
Description: R interface for the Google Cloud Services 'Document AI API'
<https://cloud.google.com/document-ai/> with additional tools for output file
parsing and text reconstruction. 'Document AI' is a powerful server-based
OCR processor that extracts text and tables from images and pdf files with
high accuracy. 'daiR' gives R users programmatic access to this processor and
additional tools to handle and visualize the output. See the package website
<https://dair.info/> for more information and examples.
Imports: base64enc, curl, fs, gargle, glue, googleCloudStorageR,
grDevices, httr, jsonlite, magick, pdftools, purrr, stringr
Suggests: covr, dplyr, knitr, ngram, qpdf, rmarkdown, sodium, testthat
Author: Thomas Hegghammer [aut, cre] (<https://orcid.org/0000-0001-6253-1518>)

Title: Data from Access to Information Law
person("Igor", "Laltuf", , "igorlaltuf@gmail.com", role = c("aut", "cre"),
Description: Downloads the public data available from the Brazilian Access to Information Law and and performs a search on information requests made since 2015.
Imports: dplyr, magrittr, plyr, R.utils, readr, stopwords, stringr,
Author: Igor Laltuf [aut, cre] (<https://orcid.org/0000-0002-5614-4404>)

Imports: dplyr, magrittr, readr, stopwords, stringr, tidytext

Description: Downloads the public data available from the Brazilian Access to Information Law and and performs a search on information requests and appeals made since 2015.

Title: Data Quality Reporting for Temporal Datasets
person(c("T.", "Phuong"), "Quan", email = "phuong.quan@ndm.ox.ac.uk",
person(family = "University of Oxford", role = "cph"),
person(family = "National Institute for Health Research (NIHR)", role = "fnd"),
Description: Generate reports that enable quick visual review of
temporal shifts in record-level data. Time series plots showing aggregated
values are automatically created for each data field (column) depending on its
contents (e.g. min/max/mean values for numeric data, no. of distinct
values for categorical data), as well as overviews for missing values,
non-conformant values, and duplicated rows. The resulting reports are shareable
and can contribute to forming a transparent record of the entire analysis process.
It is designed with Electronic Health Records in mind, but can be used for
any type of record-level temporal data (i.e. tabular data where each row represents
a single "event", one column contains the "event date", and other columns
contain any associated values for the event).
scales (>= 1.1.0), cowplot (>= 0.9.3), rmarkdown, reactable (>=
Suggests: covr, knitr, testthat (>= 3.0.0), codemetar
Author: T. Phuong Quan [aut, cre] (<https://orcid.org/0000-0001-8566-1817>),
National Institute for Health Research (NIHR) [fnd],
Maintainer: T. Phuong Quan <phuong.quan@ndm.ox.ac.uk>