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  "Title": "Counting Codes in a Text and Preparing Data for Analysis",
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  "Authors@R": "person(\"Philippe\", \"Cohard\", , \"p.cohard@laposte.net\", role = c(\"aut\", \"cre\"))",
  "Description": "Data analysis often requires coding, especially when data\nare collected through interviews, observations, or\nquestionnaires. As a result, code counting and data preparation\nare essential steps in the analysis process. Analysts may need\nto count the codes in a text (Tokenization, counting of\npre-established codes, computing the co-occurrence matrix by\nline) and prepare the data (e.g., min-max normalization,\nZ-score, robust scaling, Box-Cox transformation, and\nnon-parametric bootstrap). For the Box-Cox transformation (Box\n& Cox, 1964, <https://www.jstor.org/stable/2984418>), the\noptimal Lambda is determined using the log-likelihood method.\nNon-parametric bootstrap involves randomly sampling data with\nreplacement. Two random number generators are also integrated:\na Lehmer congruential generator for uniform distribution and a\nBox-Muller generator for normal distribution. Package for\neducational purposes.",
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  "Author": "Philippe Cohard [aut, cre]",
  "Maintainer": "Philippe Cohard <p.cohard@laposte.net>",
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      "title": "codeCount",
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