Shiny is an open-source R package that allows the easy and fast building of fully interactive web applications and webpages for data science using only R, without any knowledge of HTML, CSS, or JavaScript. Shiny in R offers numerous basic and advanced features, widgets, layouts, web app examples, and their underlying code to build upon and customize, as well as user showcases from various fields (technology, sports, banking, education, etc.) gathered and categorized by the Shiny app developer community.

Below are several approaches and how to implement them in the caret package of R. To implement these cross-validation methods in R, we need to set the method parameter of the trainControl() function to “cv”, “repeatedcv”, or “LOOCV” respectively, when defining the training control of the model.

Correlation is a measure of the strength and direction of the linear relationships between two variables. It takes values from -1 (a perfect negative correlation) to 1 (a perfect positive correlation). Covariance is a measure of the degree of how two variables change relative to each other and the direction of the linear relationships between them. Unlike correlation, covariance doesn’t have any range limit. In R, to calculate the correlation, we need to use the cor() function, to calculate the covariance—the cov() function. The syntax of both functions is identical: we need to pass in two variables (vectors) for which we want to calculate the measure (e.g., cor(vector_1, vector_2) or cov(vector_1, vector_2)), or the whole data frame, if we want to calculate the correlation or covariance between all the variables of that data frame (e.g., cor(df) or cov(df)). In the case of two vectors, the result will be a single value, in the case of a data frame, the result will be a correlation (or covariance) matrix.