Package: RefManageR
Version: 1.4.0
Title: Straightforward 'BibTeX' and 'BibLaTeX' Bibliography Management
Authors@R: person(c("Mathew", "W."), "McLean", role = c("aut", "cre"),
email = "mathew.w.mclean@gmail.com",
comment = c(ORCID = "0000-0002-7891-9645"))
Maintainer: Mathew W. McLean <mathew.w.mclean@gmail.com>
Description: Provides tools for importing and working with bibliographic
references. It greatly enhances the 'bibentry' class by providing a class
'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
encoding, and can be easily searched by any field, by date ranges, and by
various formats for name lists (author by last names, translator by full names,
etc.). Entries can be updated, combined, sorted, printed in a number of styles,
and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
'Zotero' are provided for importing references and references can be created
from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
and generating a bibliography with hyperlinks for documents prepared with
'RMarkdown' or 'RHTML'.
License: GPL-2 | GPL-3 | BSD_3_clause + file LICENSE
Imports: xml2, jsonlite, utils, plyr, tools, httr, lubridate (>=
1.5.0), stringr, methods, bibtex (>= 0.4.1)
Suggests: knitr, markdown, testthat
Encoding: UTF-8
Depends: R (>= 3.0)
VignetteBuilder: knitr
BugReports: https://github.com/ropensci/RefManageR/issues
URL: https://github.com/ropensci/RefManageR/
RoxygenNote: 7.2.1
NeedsCompilation: no
Packaged: 2022-09-30 14:06:55 UTC; MathewMcLean
Author: Mathew W. McLean [aut, cre] (<https://orcid.org/0000-0002-7891-9645>)
Repository: CRAN
Date/Publication: 2022-09-30 22:40:08 UTC

Title: Cluster analysis via regression coefficients.
Author: Weichao Bao, Xin Tong, Meredith Ray, Hongmei Zhang
Description: This package clusters regression coefficients using the methods of clustering through linear regression models (CLM) (Qin and Self 2006). Maximum likelihood approach is used to infer the parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters.