Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, <https://www.jstor.org/stable/2984418>), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes.

Tools for analyzing R expressions
  or blocks of code and determining the dependencies between them.
  It focuses on R scripts, but can be used on the bodies of functions.
  There are many facilities including the ability to summarize  or get a high-level
  view of code, determining dependencies between variables,  code improvement
  suggestions.

Functions for working with code lists and vectors with codes. These
    are an alternative for factor that keep track of both the codes and labels.
    Methods allow for transforming between codes and labels. Also supports
    hierarchical code lists.

Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned.

The 'Codemeta' Project defines a 'JSON-LD' format
    for describing software metadata, as detailed at
    <https://codemeta.github.io>. This package provides core utilities
    to generate this metadata with a minimum of dependencies.

The 'Codemeta' Project defines a 'JSON-LD' format
    for describing software metadata, as detailed at
    <https://codemeta.github.io>. This package provides utilities to
    generate, parse, and modify 'codemeta.json' files automatically for R
    packages, as well as tools and examples for working with
    'codemeta.json' 'JSON-LD' more generally.