Screens all '.R', '.Rmd', and '.qmd' files to extract the name of
    packages used in a project. This package detects packages called with 
    'library(foo)', 'require(foo)', 'foo::bar()' and 'use("foo", "bar")' and 
    adds these dependencies in the DESCRIPTION file in the sections Depends, 
    Imports, and Suggests.

The Resource Description Framework, or 'RDF' is a widely used
             data representation model that forms the cornerstone of the 
             Semantic Web. 'RDF' represents data as a graph rather than 
             the familiar data table or rectangle of relational databases.
             The 'rdflib' package provides a friendly and concise user interface
             for performing common tasks on 'RDF' data, such as reading, writing
             and converting between the various serializations of 'RDF' data,
             including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld';
             creating new 'RDF' graphs, and performing graph queries using 'SPARQL'.
             This package wraps the low level 'redland' R package which
             provides direct bindings to the 'redland' C library.  Additionally,
             the package supports the newer and more developer friendly
             'JSON-LD' format through the 'jsonld' package. The package
             interface takes inspiration from the Python 'rdflib' library.

Honest and nearly-optimal confidence intervals in fuzzy and sharp
    regression discontinuity designs and for inference at a point based on local
    linear regression. The implementation is based on Armstrong and Kolesár (2018)
    <doi:10.3982/ECTA14434>, and Kolesár and Rothe (2018)
    <doi:10.1257/aer.20160945>. Supports covariates, clustering, and weighting.

Provides a client for (1) querying the DHS API for survey indicators
  and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys
  and datasets for analysis, (3) downloading survey datasets from the DHS website,
  (4) loading datasets and associate metadata into R, and (5) extracting variables
  and combining datasets for pooled analysis.