docs(imports): Added ES6 way of importing the package and its classes

README.md

@@ -6,14 +6,14 @@ An npm package to make it easier to deal with a handful of values, and try to mo
 - [Setup](#setup)
 - [Methods](#methods)
 
-## Setup 
+## Setup
 
 ### Requirements:
   - Node.js installed<br>
  The ideal version, to run the package, is LTS(16.13 by the time it is being published),<br> however,
  older versions shouldn't have any issues, as the package does not use any <br> other npm packages, or
  fancy, new methods, not supported by older versions
- 
+
   - Installing the module<br>
 ~~~powershell
 npm install linear-regression-model
@@ -24,53 +24,59 @@ npm install linear-regression-model
 First, there is the Linear Regression model between two different datasets, in relation to each other<br>
 Which will look like something similar to this:
  ~~~ javascript
+ import {LinearModel} from 'linear-regression-model'
+ // or
  const {LinearModel} = require("linear-regression-model");
  // if it is preferred not to use destructuring, or constants use
  // var LinearModel = require("linear-regression-model-model").LinearModel;
  ~~~
 Or if the use case for this is pending more to the behavior of one single dataset<br>
 overtime, this will be more fitting:
  ~~~ javascript
+ import {LinearModelOverTime} from 'linear-regression-model'
+ // or
  const {LinearModelOverTime} = require("linear-regression-model");
  // if it is preferred not to use destructuring, or constants use
  // var LinearModel = require("linear-regression-model").LinearModelOverTime;
- ~~~ 
-And to use the Correlation class, it will be some like this: 
+ ~~~
+And to use the Correlation class, it will be some like this:
 ~~~ javascript
+ import {Correlation} from 'linear-regression-model'
+ // or
  const {Correlation} = require("linear-regression-model");
  // if it is preferred not to use destructuring, or constants use
  // var Correlation = require("linear-regression-model").Correlation;
- ~~~ 
+ ~~~
 Although if your use case is the overtime behaviour, i would advise the use<br>
 of the [method](#correlation) in the LinearModelOverTime class<br>
 But if you are using the LinearModel class, it is really up to you
 ## Instantiate the classes
  ~~~ javascript
  const lm = new LinearModel([1, 2, 3, 2, 3, 4], [3, 4, 5, 4, 5, 6]);
- // mandatory to pass two, same sized, all number, arrays, being the 
- // orientation (x, y), being x the independent variable and y the 
+ // mandatory to pass two, same sized, all number, arrays, being the
+ // orientation (x, y), being x the independent variable and y the
  // dependent: y changes according to how x changes, basically
- ~~~ 
- or 
+ ~~~
+ or
  ~~~ javascript
  const lm = new LinearModelOverTime([1, 2, 3, 2, 3, 4, 3, 4, 5, 4, 5, 6]);
- // mandatory to pass one, all number, array, being the orientation y, the 
- //dependent variable: y changes according to how x changes, the later is 
+ // mandatory to pass one, all number, array, being the orientation y, the
+ //dependent variable: y changes according to how x changes, the later is
  // generated automatically for a better representation of the behavior overtime
- ~~~ 
- or 
+ ~~~
+ or
  ~~~ javascript
   const corr = new Correlation([1, 2, 3, 2, 3, 4], [3, 4, 5, 4, 5, 6]);
- // mandatory to pass two, same sized, all number, arrays, being the 
- // orientation (x, y), being x the independent variable and y the 
+ // mandatory to pass two, same sized, all number, arrays, being the
+ // orientation (x, y), being x the independent variable and y the
  // dependent: y changes according to how x changes, basically
- ~~~ 
+ ~~~
  Let it be clear, it is necessary to pass all number arrays, with more than 1 value,<br>
  in order for the algorithm to work properly, it will try to convert all the elements<br>
  to numbers, but if that is not possible, the code will crash.
  <br>
  <br>
- 
+
 
 ## Methods
 Both the classes have similar methods, the biggest difference between the classes being<br>
@@ -83,151 +89,151 @@ the LinearModel class, the use for the LinearModelOverTime class is the exact sa
 ~~~ javascript
  lm.getDataset()
  // returns the dataset Informed in the Y axis
-~~~ 
+~~~
 
 - getXAxisValues
 ~~~ javascript
  lm.getXAxisValues()
  // returns the dataset in the X axis, informed previously or not
-~~~ 
+~~~
 - (static) radsToDegs
 ~~~ javascript
  LinearModel.radsToDegs(rad)
  // converts an angle in radians to degrees
  // returns the angle in degrees
-~~~ 
+~~~
 - (static) getMean
 ~~~ javascript
  LinearModel.getMean(dataset)
  // Utility method to calculate the mean of a dataset
-~~~ 
+~~~
 - (static) getMode
 ~~~ javascript
  LinearModel.getMode(dataset)
  // Utility method to calculate the mode of a dataset
-~~~ 
+~~~
 - (static) getMedian
 ~~~ javascript
  LinearModel.getMedian(dataset)
  // Utility method to calculate the median of a dataset
-~~~ 
+~~~
 - getDatasetLength
 ~~~ javascript
  lm.getDatasetLength()
  // returns the dataset length
-~~~ 
+~~~
 - getSumOfDatasetValues
 ~~~ javascript
  lm.getSumOfDatasetValues()
  // returns the sum of all the elements in the dataset on y azis
-~~~ 
+~~~
 - getSumOfXValues
 ~~~ javascript
  lm.getSumOfXValues()
  // returns the sum of all the elements in the dataset on x azis
-~~~ 
+~~~
 - getSlope
 ~~~ javascript
  lm.getSlope()
  // returns the slope of the equation: the "m" on the y = mx + n function
  // which is the tangent of the inclination angle
-~~~ 
+~~~
 - getAngleInRadians
 ~~~ javascript
  lm.getAngleInRadians()
  // returns the inclination angle in radians, like 3/4 meaning  3/4 rad
-~~~ 
+~~~
 - getAngleInDegrees
 ~~~ javascript
  lm.getAngleInDegrees()
- // returns the inclination angle in degrees, like 43°, 
+ // returns the inclination angle in degrees, like 43°,
  // for example 3/4 rad ~~ 43°
-~~~ 
+~~~
 - getDatasetBehavior
 ~~~ javascript
  lm.getDatasetBehavior()
  // returns the overall macro behaviour of the dataset
-~~~ 
+~~~
 - getDatasetBehavioralIntensity
 ~~~ javascript
  lm.getDatasetBehavioralIntensity()
  // returns the intensity of the behaviour previously measured
-~~~ 
+~~~
 - getLinearCoefficient
 ~~~ javascript
  lm.getDatasetBehavioralIntensity()
  // returns the intensity of the behaviour previously measured
-~~~ 
+~~~
 - getLinearCoefficient
 ~~~ javascript
  lm.getLinearCoefficient()
- // returns the linear coefficient of the equation: the "n" 
+ // returns the linear coefficient of the equation: the "n"
  //on the y = mx + n function
-~~~ 
+~~~
 - getCoefficients
 ~~~ javascript
  lm.getCoefficients()
- // returns both the linear coefficient and the slope 
+ // returns both the linear coefficient and the slope
  //of the equation: the "m" and the "n" on y = mx + n
-~~~ 
+~~~
 - getLinearEquation
 ~~~ javascript
  lm.getLinearEquation()
  // returns both the function as a string to be displayed
  // and an actual js function to make predictions, for example
  // function(x): returns Y, using the same method as the dataset
-~~~ 
+~~~
 - getR2
 ~~~ javascript
  lm.getR2()
  // returns the coefficient of determination(R²) to find
  // the accuracy of the linear regression just calculated
-~~~ 
+~~~
 ### Correlation
 - getCorrelation
 ~~~ javascript
  lm.getCorrelation()
- // returns the correlation between the datasets 
-~~~ 
+ // returns the correlation between the datasets
+~~~
 - getCorrelationInterpretation
 ~~~ javascript
  lm.getCorrelationInterpretation()
  // returns the interpretation of the correlation index
- // between the datasets 
-~~~ 
+ // between the datasets
+~~~
 
 ## Correlation (class)
 - (static) getMean
 ~~~ javascript
  Correlation.getMean(dataset)
  // returns the mean of the dataset
-~~~ 
+~~~
 - (static) getDifferenceFromMeanAndElements
 ~~~ javascript
  Correlation.getDifferenceFromMeanAndElements(dataset)
  // returns the difference between the mean and the elements
- // of the dataset, so can be used to calculate variance 
+ // of the dataset, so can be used to calculate variance
  // or standard deviation, for example
-~~~ 
+~~~
 - getCorrelation
 ~~~ javascript
  corr.getCorrelation()
- // returns the correlation between the datasets 
-~~~ 
+ // returns the correlation between the datasets
+~~~
 - getCorrelationWay
 ~~~ javascript
  corr.getCorrelationWay()
- // returns the way/sign the two datasets are correlated 
+ // returns the way/sign the two datasets are correlated
  // to each other
-~~~ 
+~~~
 - getCorrelationIntensity
 ~~~ javascript
  corr.getCorrelationIntensity()
- // returns the intensity of the correlation between the datasets 
-~~~ 
+ // returns the intensity of the correlation between the datasets
+~~~
 - getCorrelationInterpretation
 ~~~ javascript
  corr.getCorrelationInterpretation()
  // returns the interpretation of the correlation index
- // between the datasets 
-~~~ 
+ // between the datasets
+~~~

package.json

@@ -1,6 +1,6 @@
 {
     "name": "linear-regression-model",
-    "version": "1.4.0",
+    "version": "1.4.1",
     "description": "A npm package to make it easier to deal with a bunch of values, and try to model them in one of the most used mathematical models",
     "main": "index.js",
     "scripts": {