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The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham [1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data. [2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3 ...
R packages are collections of functions, documentation, and data that expand R. [16] For example, packages add report features such as RMarkdown, Quarto, [17] knitr and Sweave. Packages also add the capability to implement various statistical techniques such as linear, generalized linear and nonlinear modeling, classical statistical tests ...
The various multiple linear regression models may be compactly written as [1] = +, where Y is a matrix with ... R package and function lm() in stats package (base R)
"Glmnet: Lasso and elastic-net regularized generalized linear models" is a software which is implemented as an R source package and as a MATLAB toolbox. [ 10 ] [ 11 ] This includes fast algorithms for estimation of generalized linear models with ℓ 1 (the lasso), ℓ 2 (ridge regression) and mixtures of the two penalties (the elastic net ...
R 2 L is given by Cohen: [1] =. This is the most analogous index to the squared multiple correlations in linear regression. [3] It represents the proportional reduction in the deviance wherein the deviance is treated as a measure of variation analogous but not identical to the variance in linear regression analysis. [3]
4 Regression. 5 Time series analysis. 6 Charts ... The following tables compare general and technical information for a number of statistical analysis packages ...
The group of packages strives to provide a cohesive collection of functions to deal with common data science tasks, including data import, cleaning, transformation and visualisation (notably with the ggplot2 package). The R Infrastructure packages [31] support coding and the development of R packages and as of 2021-05-04, Metacran [17] lists 16 ...
Random forests can be used to rank the importance of variables in a regression or classification problem in a natural way. The following technique was described in Breiman's original paper [7] and is implemented in the R package randomForest. [8]