Density, distribution function, quantile function and random 
    generation for the Nakagami distribution of Nakagami (1960) 
    <doi:10.1016/B978-0-08-009306-2.50005-4>.

Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.

A system for organizing column names in data. Aimed at supporting a 
  prefix-based and suffix-based column naming scheme. Extends 'dplyr' functionality
  to add ordering by function and more explicit renaming.

Provides 'visual citations' containing the metadata of a
    scientific paper and a 'QR' code.  A 'visual citation' is a banner
    containing title, authors, journal and year of a publication.  This
    package can create such banners based on 'BibTeX' and 'BibLaTeX'
    references or call the reference metadata from 'Crossref'-API. The
    banners include a QR code pointing to the 'DOI'.  The resulting HTML
    object or PNG image can be included in a presentation to point the
    audience to good resources for further reading.  Styling is possible
    via predefined designs or via custom 'CSS'.  This package is not
    intended as replacement for proper reference manager packages, but a
    tool to enrich scientific presentation slides and conference posters.

The Needleman-Wunsch global alignment algorithm can be
  used to find approximate matches between sample names in different
  data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.

It names the 'R Markdown' chunks of files based on the
    filename.

Wraps the 'nametag' library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models.
    Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.