Curates biological sequences massively, quickly, without errors and 
 without internet connection.  Biological sequences curing is performed by 
 aligning the forward and / or revers primers or ends of cloning vectors with the sequences 
 to be cleaned. After the alignment, new subsequences are generated without biological fragment 
 not desired by the user.
 Pozzi et al (2020) <doi:10.1007/s00438-020-01671-z>.

Wrapper of .Call() that runs exit handlers to clean up C
    resources. Helps managing C (non-R) resources while using the R API.

Functions to work with data frames to prepare data for further analysis.
    The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.

Data cleaning functions for classes logical,
  factor, numeric, character, currency and Date to make
  data cleaning fast and easy. Relying on very few dependencies, it 
  provides smart guessing, but with user options to override 
  anything if needed.

Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issue with sp objects.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.

Provides a set of fast tools for converting a textual corpus into
  a set of normalized tables. Users may make use of the 'udpipe' back end with
  no external dependencies, or two Python back ends with 'spaCy'
  <https://spacy.io> or 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/>.
  Exposed annotation tasks include tokenization, part of speech tagging, named
  entity recognition, and dependency parsing.

Check your R code for some of the most common
    layout flaws.  Many tried to teach us how to write code less dreadful,
    be it implicitly as B. W. Kernighan and D. M. Ritchie (1988)
    <ISBN:0-13-110362-8> in 'The C Programming Language' did, be it
    explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in 'Clean Code:
    A Handbook of Agile Software Craftsmanship' did.  So we should check
    our code for files too long or wide, functions with too many lines,
    too wide lines, too many arguments or too many levels of nesting.
    Note: This is not a static code analyzer like pylint or the like.
    Checkout <https://cran.r-project.org/package=lintr> instead.