Learn R Programming

⚠️There's a newer version (1.19) of this package.Take me there.

liver

The R package liver provides a collection of helper functions that make various techniques from data science more user-friendly for non-experts. In this way, our aim is to allow non-experts to become familiar with the techniques with only a minimal level of coding knowledge. Indeed, following an ancient Persian idiom, we refer to this as "eating the liver of data science" which could be interpreted as "getting intimately close with data science". Examples of procedures we include are: data partitioning for out-of-sample testing, computing Mean Squared Error (MSE) for quantifying prediction accuracy, and data transformation (z-score and min-max). Besides such helper functions, the package also includes several interesting datasets that are useful for multivariate analysis.

Installation

To install the latest version of this package from CRAN, do the following from the R console:

install.packages( "liver" )

Loading

require( liver )

Copy Link

Version

Install

install.packages('liver')

Monthly Downloads

578

Version

1.9

License

GPL (>= 2)

Maintainer

Abdolreza Mohammadi

Last Published

September 12th, 2021

Functions in liver (1.9)

accuracy

Average classification accuracy
bank

Bank marketing data set
corona

Corona data set
advertising

advertising data set
conf.mat

Confusion Matrix
conf.mat.plot

Plot Confusion Matrix
churnTel

churnTel dataset
skewness

Skewness
adult

adult data set
cereal

Cereal data set
kNN.plot

Visualizing the Optimal Number of k
transform

Z-score normalization
housePrice

housePrice dataset
house

house data set
churn

Churn data set
mae

Mean Absolute Error (MAE)
marketing

marketing data set
liver-package

liver: "Eating the Liver of Data Science"
mse

Mean Squared Error (MSE)
minmax

Min-Max normalization
find.na

find.na
insurance

insurance data set
zscore

Z-score normalization
fertilizer

Fertilizer data set
partition

Partition the data
kNN

k-Nearest Neighbour Classification
risk

Risk data set