Version 2.0

This version contains some breaking changes:

  * Multi-modal datasets are returned for `mode`, as ES6 Sets
  * With the use of ES6 Sets and const/let, this requires Node v4.0.0+

New Features:

  * Support for multi-modal datasets (Fixes #1)
  * Cleanup and ES6-ification
  * Dependency update (tape)

.npmignore

@@ -1,2 +1,3 @@
 examples
 test
+.npmignore

README.md

@@ -40,6 +40,8 @@ standard deviation: 2.430499811424252
 
 ```
 
+**Compatibility Notice**: Version 2.0.0+ of this library use features that require Node.js v4.0.0 and above
+
 API
 ===
 
@@ -65,17 +67,19 @@ numbers(["cat", 1, "22.9", 9])
 `mean(vals)`
 ---
 
-Calculate the [mean](http://en.wikipedia.org/wiki/Mean) average value of vals.
+Calculate the [mean](http://en.wikipedia.org/wiki/Mean) average value of `vals`.
 
 `median(vals)`
 ---
 
-Calculate the [median](http://en.wikipedia.org/wiki/Median) average value of vals.
+Calculate the [median](http://en.wikipedia.org/wiki/Median) average value of `vals`.
 
 `mode(vals)`
 ---
 
-Calculate the [mode](http://en.wikipedia.org/wiki/Mode_statistics) average value of vals.
+Calculate the [mode](http://en.wikipedia.org/wiki/Mode_statistics) average value of `vals`.
+
+If `vals` is multi-modal (contains multiple modes), `mode(vals)` will return a ES6 Set of the modes.
 
 `variance(vals)`
 ---

package.json

@@ -4,6 +4,7 @@
   "description": "A light statistical package that operates on numeric Arrays.",
   "main": "stats.js",
   "directories": {
+    "example": "examples",
     "test": "test"
   },
   "scripts": {
@@ -32,10 +33,14 @@
   "bugs": {
     "url": "https://github.com/brycebaril/node-stats-lite/issues"
   },
+  "engines": {
+    "node": ">=2.0.0"
+  },
   "dependencies": {
     "isnumber": "~1.0.0"
   },
   "devDependencies": {
-    "tape": "~2.10.2"
-  }
+    "tape": "~4.2.0"
+  },
+  "homepage": "https://github.com/brycebaril/node-stats-lite"
 }

stats.js

@@ -1,3 +1,5 @@
+"use strict";
+
 module.exports.numbers = numbers
 module.exports.sum = sum
 module.exports.mean = mean
@@ -7,28 +9,28 @@ module.exports.variance = variance
 module.exports.stdev = stdev
 module.exports.percentile = percentile
 
-var isNumber = require("isnumber")
+const isNumber = require("isnumber")
 
 function numbers(vals) {
-  var nums = []
+  let nums = []
   if (vals == null)
     return nums
 
-  for (var i = 0; i < vals.length; i++) {
+  for (let i = 0; i < vals.length; i++) {
     if (isNumber(vals[i]))
       nums.push(+vals[i])
   }
   return nums
 }
 
 function nsort(vals) {
-  return vals.sort(function (a, b) { return a - b })
+  return vals.sort(function numericSort(a, b) { return a - b })
 }
 
 function sum(vals) {
   vals = numbers(vals)
-  var total = 0
-  for (var i = 0; i < vals.length; i++) {
+  let total = 0
+  for (let i = 0; i < vals.length; i++) {
     total += vals[i]
   }
   return total
@@ -44,7 +46,7 @@ function median(vals) {
   vals = numbers(vals)
   if (vals.length === 0) return NaN
 
-  var half = (vals.length / 2) | 0
+  let half = (vals.length / 2) | 0
 
   vals = nsort(vals)
   if (vals.length % 2) {
@@ -57,32 +59,50 @@ function median(vals) {
   }
 }
 
-// Returns the mode of a unimodal dataset -- NaN for multi-modal or empty datasets.
+// Returns the mode of a unimodal dataset
+// If the dataset is multi-modal, returns a Set containing the modes
 function mode(vals) {
   vals = numbers(vals)
   if (vals.length === 0) return NaN
-  var mode = NaN
-  var dist = {}
-  vals.forEach(function (n) {
-    var me = dist[n] || 0
+  let mode = NaN
+  let dist = {}
+
+  for (let i = 0; i < vals.length; i++) {
+    let value = vals[i]
+    let me = dist[value] || 0
     me++
-    dist[n] = me
-  })
-  var rank = numbers(Object.keys(dist).sort(function (a, b) { return dist[b] - dist[a] }))
+    dist[value] = me
+  }
+
+  let rank = numbers(Object.keys(dist).sort(function sortMembers(a, b) { return dist[b] - dist[a] }))
   mode = rank[0]
   if (dist[rank[1]] == dist[mode]) {
-    // Multiple modes found, abort
-    return NaN
+    // multi-modal
+    if (rank.length == vals.length) {
+      // all values are modes
+      return vals
+    }
+    let modes = new Set([mode])
+    let modeCount = dist[mode]
+    for (let i = 1; i < rank.length; i++) {
+      if (dist[rank[i]] == modeCount) {
+        modes.add(rank[i])
+      }
+      else {
+        break
+      }
+    }
+    return modes
   }
   return mode
 }
 
 // Variance = average squared deviation from mean
 function variance(vals) {
   vals = numbers(vals)
-  var avg = mean(vals)
-  var diffs = []
-  for (var i = 0; i < vals.length; i++) {
+  let avg = mean(vals)
+  let diffs = []
+  for (let i = 0; i < vals.length; i++) {
     diffs.push(Math.pow((vals[i] - avg), 2))
   }
   return mean(diffs)
@@ -100,10 +120,10 @@ function percentile(vals, ptile) {
   // Fudge anything over 100 to 1.0
   if (ptile > 1) ptile = 1
   vals = nsort(vals)
-  var i = (vals.length * ptile) - 0.5
+  let i = (vals.length * ptile) - 0.5
   if ((i | 0) === i) return vals[i]
   // interpolated percentile -- using Estimation method
-  var int_part = i | 0
-  var fract = i - int_part
+  let int_part = i | 0
+  let fract = i - int_part
   return (1 - fract) * vals[int_part] + fract * vals[int_part + 1]
-}
+}

test/index.js

@@ -1,9 +1,11 @@
-var test = require("tape").test
+"use strict";
 
-var stats = require("../stats")
+const test = require("tape").test
+
+const stats = require("../stats")
 
 test("numbers", function (t) {
-  var numbers = stats.numbers
+  let numbers = stats.numbers
   t.equals(typeof numbers, "function", "numbers is a function")
 
   t.deepEquals(numbers(), [], "undefined returns empty array")
@@ -18,7 +20,7 @@ test("numbers", function (t) {
 })
 
 test("sum", function (t) {
-  var sum = stats.sum
+  let sum = stats.sum
 
   t.equals(typeof sum, "function", "sum is a function")
 
@@ -34,13 +36,13 @@ test("sum", function (t) {
 })
 
 test("mean", function (t) {
-  var mean = stats.mean
+  let mean = stats.mean
 
   t.equals(typeof mean, "function", "mean is a function")
 
   t.ok(isNaN(mean()), "mean of nothing is NaN")
 
-  t.ok(isNaN(mean([])), NaN, "mean of nothing is NaN")
+  t.ok(isNaN(mean([])), "mean of nothing is NaN")
 
   t.deepEquals(mean([1, 2, 3]), 2, "mean works")
 
@@ -50,13 +52,13 @@ test("mean", function (t) {
 })
 
 test("median", function (t) {
-  var median = stats.median
+  let median = stats.median
 
   t.equals(typeof median, "function", "median is a function")
 
   t.ok(isNaN(median()), "median of nothing is NaN")
 
-  t.ok(isNaN(median([])), NaN, "median of nothing is NaN")
+  t.ok(isNaN(median([])), "median of nothing is NaN")
 
   t.deepEquals(median([1, 2, 2, 2, 3, 14]), 2, "median works")
 
@@ -68,31 +70,33 @@ test("median", function (t) {
 })
 
 test("mode", function (t) {
-  var mode = stats.mode
+  let mode = stats.mode
 
   t.equals(typeof mode, "function", "mode is a function")
 
   t.ok(isNaN(mode()), "mode of nothing is NaN")
 
-  t.ok(isNaN(mode([])), NaN, "mode of nothing is NaN")
+  t.ok(isNaN(mode([])), "mode of nothing is NaN")
 
   t.deepEquals(mode([1, 2, 2, 2, 3, 14]), 2, "mode works")
 
-  t.ok(isNaN(mode([1, 2, 7, 8, 5])), "gives up for multiple modes")
+  t.deepEquals(mode([1, 1, 7, 5, 5, 8, 7]), new Set([1, 5, 7]), "multi-modal works")
+
+  t.deepEquals(mode([1, 1, 7, 5, 5, 8, 7]), new Set([1, 7, 5]), "multi-modal works and order doesn't matter (yay Set)")
 
-  t.deepEquals(mode([1, "6", 2, 8, 7, 2]), 2, "mode works (even) number")
+  t.deepEquals(mode([1, "6", 2, 8, 7, 2]), 2, "mode works with stringification")
 
   t.end()
 })
 
 test("variance", function (t) {
-  var variance = stats.variance
+  let variance = stats.variance
 
   t.equals(typeof variance, "function", "variance is a function")
 
   t.ok(isNaN(variance()), "variance of nothing is NaN")
 
-  t.ok(isNaN(variance([])), NaN, "variance of nothing is NaN")
+  t.ok(isNaN(variance([])), "variance of nothing is NaN")
 
   t.deepEquals(variance([2, 4, 4, 4, 5, 5, 7, 9]), 4, "variance works")
 
@@ -102,13 +106,13 @@ test("variance", function (t) {
 })
 
 test("stdev", function (t) {
-  var stdev = stats.stdev
+  let stdev = stats.stdev
 
   t.equals(typeof stdev, "function", "stdev is a function")
 
   t.ok(isNaN(stdev()), "stdev of nothing is NaN")
 
-  t.ok(isNaN(stdev([])), NaN, "stdev of nothing is NaN")
+  t.ok(isNaN(stdev([])), "stdev of nothing is NaN")
 
   t.deepEquals(stdev([2, 4, 4, 4, 5, 5, 7, 9]), 2, "stdev works")
 
@@ -118,17 +122,17 @@ test("stdev", function (t) {
 })
 
 test("percentile", function (t) {
-  var percentile = stats.percentile
+  let percentile = stats.percentile
 
   t.equals(typeof percentile, "function", "percentile is a function")
 
   t.ok(isNaN(percentile()), "percentile of nothing is NaN")
 
-  t.ok(isNaN(percentile([])), NaN, "percentile of nothing is NaN")
+  t.ok(isNaN(percentile([])), "percentile of nothing is NaN")
 
-  var scores = [4,4,5,5,5,5,6,6,6,7,7,7,8,8,9,9,9,10,10,10]
+  let scores = [4,4,5,5,5,5,6,6,6,7,7,7,8,8,9,9,9,10,10,10]
 
-  t.ok(isNaN(percentile(scores)), NaN, "percentile requires a target percentile")
+  t.ok(isNaN(percentile(scores)), "percentile requires a target percentile")
 
 
   t.deepEquals(percentile(scores, 0.50), stats.median(scores), "50th percentile equals median")
@@ -142,4 +146,4 @@ test("percentile", function (t) {
   t.deepEquals(percentile([15, 20, 35, 40, 50], 0.4), 27.5, "perentile works")
 
   t.end()
-})
+})