A likelihood-based hypothesis testing approach is implemented for
  assessing causal mediation. Described in Millstein, Chen, and Breton (2016),
  <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation
  of a known causal association between a DNA variant, the 'instrumental variable',
  and a clinical outcome or phenotype by gene expression or DNA methylation, the
  potential mediator. Another example would be testing mediation of the effect
  of a drug on a clinical outcome by the molecular target. The hypothesis test
  generates a p-value or permutation-based FDR value with confidence intervals
  to quantify uncertainty in the causal inference. The outcome can be represented
  by either a continuous or binary variable, the potential mediator is continuous,
  and the instrumental variable can be continuous or binary and is not limited to
  a single variable but may be a design matrix representing multiple variables.

Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modelling phenomena encountered in the social sciences.
    This package is deprecated, see 'agop' instead.

A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.

In searching for research articles, we often want to 
  obtain lists of references from across studies, and also obtain lists 
  of articles that cite a particular study. In systematic reviews, this 
  supplementary search technique is known as 'citation chasing': forward 
  citation chasing looks for all records citing one or more articles of 
  known relevance; backward citation chasing looks for all records 
  referenced in one or more articles. Traditionally, this process would 
  be done manually, and the resulting records would need to be checked 
  one-by-one against included studies in a review to identify potentially 
  relevant records that should be included in a review. This package 
  contains functions to automate this process by making use of the 
  Lens.org API. An input article list can be used to return a list of 
  all referenced records, and/or all citing records in the Lens.org 
  database (consisting of PubMed, PubMed Central, CrossRef, Microsoft 
  Academic Graph and CORE; <https://www.lens.org>).