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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 |
| Reference manual: | codecountR.pdf |
| Vignettes: |
How_to_use_codeCountR (source, R code) |
| 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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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