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>).

Client for the Open Citations Corpus (<http://opencitations.net/>).
    Includes a set of functions for getting one identifier type from another,
    as well as getting references and citations for a given identifier.

Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.

A systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. 'CITMIC' first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.