codecountR: Counting Codes in a Text and Preparing Data for Analysis

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 and counting of pre-established codes) 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.

Version:	0.0.4.5
Imports:	stats
Suggests:	knitr, rmarkdown
Published:	2024-10-16
DOI:	10.32614/CRAN.package.codecountR
Author:	Philippe Cohard [aut, cre]
Maintainer:	Philippe Cohard <p.cohard at laposte.net>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	codecountR results

Documentation:

Reference manual:	codecountR.pdf
Vignettes:	How_to_use_codeCountR (source, R code)

Downloads:

Package source:	codecountR_0.0.4.5.tar.gz
Windows binaries:	r-devel: codecountR_0.0.4.5.zip, r-release: codecountR_0.0.4.5.zip, r-oldrel: codecountR_0.0.4.5.zip
macOS binaries:	r-release (arm64): codecountR_0.0.4.5.tgz, r-oldrel (arm64): codecountR_0.0.4.5.tgz, r-release (x86_64): codecountR_0.0.4.5.tgz, r-oldrel (x86_64): codecountR_0.0.4.5.tgz
Old sources:	codecountR archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=codecountR to link to this page.