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dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
The tag name can be any sequence of alphanumeric characters that may be used to indicate how the enclosed block is to be deciphered. For example, #<latex> could indicate the start of a block of LaTeX formatted documentation.
In the C standard library, abbreviated names are the most common (e.g. isalnum for a function testing whether a character is alphanumeric), while the C++ standard library often uses an underscore as a word separator (e.g. out_of_range).
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
ggplot2 is an open-source data visualization package for the statistical programming language R.Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a ...
knitr is a software engine for dynamic report generation with R. [1] [2] It is a package in the programming language R that enables integration of R code into LaTeX, LyX, HTML, Markdown, AsciiDoc, and reStructuredText documents. The purpose of knitr is to allow reproducible research in R through the means of literate programming.
It is used if the prediction logic itself did not produce a result. This can happen, e.g., if an input value is missing and there is no other method for treating missing values. Output: this element can be used to name all the desired output fields expected from the model. These are features of the predicted field and so are typically the ...
where a 1 = 0.0705230784, a 2 = 0.0422820123, a 3 = 0.0092705272, a 4 = 0.0001520143, a 5 = 0.0002765672, a 6 = 0.0000430638 erf x ≈ 1 − ( a 1 t + a 2 t 2 + ⋯ + a 5 t 5 ) e − x 2 , t = 1 1 + p x {\displaystyle \operatorname {erf} x\approx 1-\left(a_{1}t+a_{2}t^{2}+\cdots +a_{5}t^{5}\right)e^{-x^{2}},\quad t={\frac {1}{1+px ...