[DOC] Documentation for builtin Outlier function
Closes #940.
diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index 4b56fb3..c8fb028 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -35,6 +35,7 @@
* [`normalize`-Function](#normalize-function)
* [`gnmf`-Function](#gnmf-function)
* [`msvm`-Function](#msvm-function)
+ * [`outlier`-Function](#outlier-function)
* [`toOneHot`-Function](#toOneHOt-function)
@@ -413,6 +414,7 @@
w = lm(X = X, y = y)
ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
```
+
## `normalize`-Function
The `normalize`-function normalises the values of a matrix by changing the dataset to use a common scale.
@@ -478,6 +480,33 @@
gnmf(X = X, rnk = 2, eps = 10^-8, maxi = 10)
```
+## `outlier`-Function
+
+This `outlier`-function takes a matrix data set as input from where it determines which point(s)
+have the largest difference from mean.
+
+### Usage
+```r
+outlier(X, opposite)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :------- | :------------- | -------- | :---------- |
+| X | Matrix[Double] | required | Matrix of Recoded dataset for outlier evaluation |
+| opposite | Boolean | required | (1)TRUE for evaluating outlier from upper quartile range, (0)FALSE for evaluating outlier from lower quartile range |
+
+### Returns
+| Type | Description |
+| :------------- | :---------- |
+| Matrix[Double] | matrix indicating outlier values |
+
+### Example
+```r
+X = rand (rows = 50, cols = 10)
+outlier(X=X, opposite=1)
+```
+
## `toOneHot`-Function
The `toOneHot`-function encodes unordered categorical vector to multiple binarized vectors.
