What are the references for canonical correlation analysis of R language

Today, I will talk to you about the reference materials about the canonical correlation analysis of R language, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following contents for you. I hope you can gain something according to this article.

Canonical correlation analysis (Canonical Correlation analysis, CCA) is a statistical method to study the correlation between two groups of variables. If each set of variables contains only one variable, the correlation can be measured by the correlation coefficient. However, the number of variables in each group of variables is greater than 1, such as temperature and humidity in group An and DBH in group B. to measure the correlation between these two groups of variables, we can use the idea of principal components to transform the correlation between the two groups of variables into the maximum possible correlation of the two comprehensive variables, namely canonical correlation analysis (CCA). -- extracted from Applied Statistical Analysis and R language practice

The text description is a little verbose. Look directly at the above picture from the tutorial http://my.ilstu.edu/~wjschne/444/CanonicalCorrelation.html#(4) example and R language implementation of the first small example: "Applied Statistical Analysis and R language practice" Chapter 8 Section 6 case title: study on the relationship between Children's Morphology and Pulmonary ventilatory function data healthy Children Morphology, Pulmonary ventilation function, height x1 (cm) vital capacity y1 (L) weight x2 (kg) resting ventilation y2 (L) chest circumference x3 (cm) maximum ventilation per minute y3 (L) code df