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 a Python back ends with 'spaCy' <https://spacy.io>.
  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.

A collection of clean 'R Markdown' HTML document templates
    using classy-looking classless CSS styles. These documents use a
    minimal set of dependencies but still look great, making them suitable
    for use a package vignettes or for sharing results via email.

A reliable and efficient tool for cleaning univariate time 
    series data. It implements reliable and efficient procedures for 
    automating the process of cleaning univariate time series data. 
    The package provides integration with already developed and deployed 
    tools for missing value imputation and outlier detection. It also 
    provides a way of visualizing large time-series data in different 
    resolutions.

Small package to clean the R console and the R environment
    with the call of just one function.

Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.