Re-Scale Vectors and Time-Series Features

## Installation

You can install the stable version of `normaliseR`

from
CRAN:

`install.packages("normaliseR")`

You can install the development version of `normaliseR`

from GitHub using the following:

`devtools::install_github("hendersontrent/normaliseR")`

## General purpose

`normaliseR`

is a software package for R for rescaling
numerical vectors or `feature_calculations`

objects produced
by the `theft`

R
package for computing time-series features.

Putting calculated feature vectors on an equal scale is crucial for
any statistical or machine learning model as variables with high
variance can adversely impact the model’s capacity to fit the data
appropriately, learn appropriate weight values, or minimise a loss
function. `normaliseR`

includes function
`normalise`

(or `normalize`

) to rescale either a
whole `feature_calculations`

object, or a single vector of
values. The following normalisation methods are currently offered:

- z-score—
`"zScore"`

- Sigmoid—
`"Sigmoid"`

- Outlier-robust Sigmoid (credit to Ben Fulcher for creating the
original MATLAB
version) –
`"RobustSigmoid"`

- Min-max—
`"MinMax"`

- Maximum absolute—
`"MaxAbs"`