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5 Most popular R libraries you should know

R programming language is widely used by statisticians and data scientists as an effective tool for conducting data mining and statistical data analysis.

R is a programming language for statistical computing, data analysis, and machine learning. It is a simple, well-developed, and highly efficient programming language with excellent data handling and the finest storage capability.

It is a group of operations used to calculate arrays, lists, vectors, and matrices. R enables integration with code written in algebraic languages, C, C++,.NET, Python, and other programming languages.

Therefore R is more robust and a preferred programming language by developers nowadays. So let’s look at the top 5 R libraries that everyone should know.

webmorphR
It helps to make the construction of an image more stimuli and consistent. Although the stimuli used cannot be shared, it allows sharing recipes for creating stimuli, encouraging generalisability to new faces.

Metrica
This package consists of over 80 functions and is designed to calculate the prediction performance of regression and classification point-forecast models such as DNDC, APSIM, DSSAT, and more.

cito
It helps developers to build and train neural networks with the standard R syntax. Apart from this, all the generic R methods can be used on the created object. cito is based on the torch framework.

etree
It gives a user-friendly implementation of Energy Trees, a model for regression and classification with mixed-type and structured data. Currently, the package includes graphs and functions as structured covariates.

mildsvm
This package provides the easiest way to learn from data by providing training to Support Vector Machine (SVM)-based classifiers. Apart from this, it has useful functions for building and printing multiple instance data frames.

Apart from the above-mentioned libraries, R has powerful packages for data manipulation and data visualization that helps reduce human efforts. These packages are plyr, dplyr, tidyr, ggplot, and plotly.

For more such content, visit: https://bit.ly/3ijY5Gt

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