congruGen

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.

Philippe Cohard

codecountR

Counting Codes in a Text and Preparing Data for Analysis

congruGen function

<dl><dt>seed</dt>
<dd>a number</dd>
<dt>a</dt>
<dd>a number</dd></dl>

Arguments

congruGen — congruGen

<dl>

<dt>seed</dt>
<dd>a number</dd>


<dt>a</dt>
<dd>a number</dd>

</dl>

congruGen: congruGen

Description

Usage

Value

Arguments

Examples