@sgratzl/boxplots - npm Package Compare versions

Comparing version 1.2.2 to 1.3.0

build/index.d.ts

@@ -8,2 +8,9 @@ /**
 
+declare type KernelDensityEstimator = (v: number) => number;
+declare function kde(stats: {
+    items: ArrayLike<number>;
+    iqr: number;
+    variance: number;
+}): KernelDensityEstimator;
+
 interface IBoxPlot {
@@ -53,2 +60,6 @@ /**
     /**
+     * variance
+     */
+    readonly variance: number;
+    /**
      * number of missing values (NaN, null, undefined) in the data
@@ -65,2 +76,3 @@ */
     readonly items: ArrayLike<number>;
+    readonly kde: KernelDensityEstimator;
 }
@@ -106,2 +118,7 @@ declare interface QuantileMethod {
 
+interface QuantilesResult {
+    q1: number;
+    median: number;
+    q3: number;
+}
 /**
@@ -112,7 +129,3 @@ * computes the boxplot stats using the given interpolation function if needed
  */
-declare function quantilesInterpolate(arr: ArrayLike<number>, length: number, interpolate: (i: number, j: number, fraction: number) => number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesInterpolate(arr: ArrayLike<number>, length: number, interpolate: (i: number, j: number, fraction: number) => number): QuantilesResult;
 /**
@@ -122,7 +135,3 @@ * Uses R's quantile algorithm type=7.
  */
-declare function quantilesType7(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesType7(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
@@ -132,39 +141,19 @@ * ‘linear’: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
  */
-declare function quantilesLinear(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesLinear(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
  * ‘lower’: i.
  */
-declare function quantilesLower(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesLower(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
  * 'higher': j.
  */
-declare function quantilesHigher(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesHigher(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
  * ‘nearest’: i or j, whichever is nearest
  */
-declare function quantilesNearest(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesNearest(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
  * ‘midpoint’: (i + j) / 2
  */
-declare function quantilesMidpoint(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesMidpoint(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
@@ -177,7 +166,3 @@ * The hinges equal the quartiles for odd n (where n <- length(x))
  */
-declare function quantilesFivenum(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesFivenum(arr: ArrayLike<number>, length?: number): QuantilesResult;
 /**
@@ -188,10 +173,5 @@ * alias for quantilesFivenum
  */
-declare function quantilesHinges(arr: ArrayLike<number>, length?: number): {
-    q1: number;
-    median: number;
-    q3: number;
-};
+declare function quantilesHinges(arr: ArrayLike<number>, length?: number): QuantilesResult;
 
-export default boxplot;
-export { BoxplotStatsOptions, IBoxPlot, QuantileMethod, boxplot, quantilesFivenum, quantilesHigher, quantilesHinges, quantilesInterpolate, quantilesLinear, quantilesLower, quantilesMidpoint, quantilesNearest, quantilesType7 };
+export { BoxplotStatsOptions, IBoxPlot, KernelDensityEstimator, QuantileMethod, QuantilesResult, boxplot, boxplot as default, kde, quantilesFivenum, quantilesHigher, quantilesHinges, quantilesInterpolate, quantilesLinear, quantilesLower, quantilesMidpoint, quantilesNearest, quantilesType7 };
 //# sourceMappingURL=index.d.ts.map

build/index.js

@@ -8,6 +8,30 @@ /**
 
-'use strict';
+const HELPER = Math.sqrt(2 * Math.PI);
+function gaussian(u) {
+    return Math.exp(-0.5 * u * u) / HELPER;
+}
+function toSampleVariance(variance, len) {
+    return (variance * len) / (len - 1);
+}
+function nrd(iqr, variance, len) {
+    let s = Math.sqrt(toSampleVariance(variance, len));
+    if (typeof iqr === 'number') {
+        s = Math.min(s, iqr / 1.34);
+    }
+    return 1.06 * s * Math.pow(len, -0.2);
+}
+function kde(stats) {
+    const len = stats.items.length;
+    const bandwidth = nrd(stats.iqr, stats.variance, len);
+    return (x) => {
+        let i = 0;
+        let sum = 0;
+        for (i = 0; i < len; i++) {
+            const v = stats.items[i];
+            sum += gaussian((x - v) / bandwidth);
+        }
+        return sum / bandwidth / len;
+    };
+}
 
-Object.defineProperty(exports, '__esModule', { value: true });
-
 function quantilesInterpolate(arr, length, interpolate) {
@@ -61,9 +85,6 @@ const n1 = length - 1;
 function createSortedData(data) {
-    let min = Number.POSITIVE_INFINITY;
-    let max = Number.NEGATIVE_INFINITY;
-    let sum = 0;
     let valid = 0;
-    const length = data.length;
+    const { length } = data;
     const vs = data instanceof Float64Array ? new Float64Array(length) : new Float32Array(length);
-    for (let i = 0; i < length; ++i) {
+    for (let i = 0; i < length; i += 1) {
         const v = data[i];
@@ -74,10 +95,3 @@ if (v == null || Number.isNaN(v)) {
         vs[valid] = v;
-        valid++;
-        if (v < min) {
-            min = v;
-        }
-        if (v > max) {
-            max = v;
-        }
-        sum += v;
+        valid += 1;
     }
@@ -87,5 +101,4 @@ const missing = length - valid;
         return {
-            sum,
-            min,
-            max,
+            min: Number.NaN,
+            max: Number.NaN,
             missing,
@@ -95,10 +108,11 @@ s: [],
     }
-    const s = valid === length ? vs : vs.subarray(0, valid);
-    s.sort((a, b) => (a === b ? 0 : a < b ? -1 : 1));
+    const validData = valid === length ? vs : vs.subarray(0, valid);
+    validData.sort((a, b) => (a === b ? 0 : a < b ? -1 : 1));
+    const min = validData[0];
+    const max = validData[validData.length - 1];
     return {
-        sum,
-        min: s[0],
-        max: s[s.length - 1],
+        min,
+        max,
         missing,
-        s,
+        s: validData,
     };
@@ -109,3 +123,2 @@ }
         return {
-            sum: Number.NaN,
             min: Number.NaN,
@@ -119,10 +132,3 @@ max: Number.NaN,
     const max = data[data.length - 1];
-    const red = (acc, v) => acc + v;
-    const sum = data instanceof Float32Array
-        ? data.reduce(red, 0)
-        : data instanceof Float64Array
-            ? data.reduce(red, 0)
-            : data.reduce(red, 0);
     return {
-        sum,
         min,
@@ -134,4 +140,67 @@ max,
 }
+function computeWhiskers(s, valid, min, max, { eps, quantiles, coef, whiskersMode }) {
+    const same = (a, b) => Math.abs(a - b) < eps;
+    const { median, q1, q3 } = quantiles(s, valid);
+    const iqr = q3 - q1;
+    const isCoefValid = typeof coef === 'number' && coef > 0;
+    let whiskerLow = isCoefValid ? Math.max(min, q1 - coef * iqr) : min;
+    let whiskerHigh = isCoefValid ? Math.min(max, q3 + coef * iqr) : max;
+    const outlierLow = [];
+    for (let i = 0; i < valid; i += 1) {
+        const v = s[i];
+        if (v >= whiskerLow || same(v, whiskerLow)) {
+            if (whiskersMode === 'nearest') {
+                whiskerLow = v;
+            }
+            break;
+        }
+        if (outlierLow.length === 0 || !same(outlierLow[outlierLow.length - 1], v)) {
+            outlierLow.push(v);
+        }
+    }
+    const reversedOutlierHigh = [];
+    for (let i = valid - 1; i >= 0; i -= 1) {
+        const v = s[i];
+        if (v <= whiskerHigh || same(v, whiskerHigh)) {
+            if (whiskersMode === 'nearest') {
+                whiskerHigh = v;
+            }
+            break;
+        }
+        if ((reversedOutlierHigh.length === 0 || !same(reversedOutlierHigh[reversedOutlierHigh.length - 1], v)) &&
+            (outlierLow.length === 0 || !same(outlierLow[outlierLow.length - 1], v))) {
+            reversedOutlierHigh.push(v);
+        }
+    }
+    const outlier = outlierLow.concat(reversedOutlierHigh.reverse());
+    return {
+        median,
+        q1,
+        q3,
+        iqr,
+        outlier,
+        whiskerHigh,
+        whiskerLow,
+    };
+}
+function computeStats(s, valid) {
+    let mean = 0;
+    for (let i = 0; i < valid; i++) {
+        const v = s[i];
+        mean += v;
+    }
+    mean /= valid;
+    let variance = 0;
+    for (let i = 0; i < valid; i++) {
+        const v = s[i];
+        variance += (v - mean) * (v - mean);
+    }
+    variance /= valid;
+    return {
+        mean,
+        variance,
+    };
+}
 function boxplot(data, options = {}) {
-    const { quantiles, validAndSorted, coef, whiskersMode, eps } = Object.assign({
+    const fullOptions = {
         coef: 1.5,
@@ -142,4 +211,5 @@ eps: 10e-3,
         whiskersMode: 'nearest',
-    }, options);
-    const { missing, s, min, max, sum } = validAndSorted ? withSortedData(data) : createSortedData(data);
+        ...options,
+    };
+    const { missing, s, min, max } = fullOptions.validAndSorted ? withSortedData(data) : createSortedData(data);
     const invalid = {
@@ -158,5 +228,6 @@ min: Number.NaN,
         q3: Number.NaN,
+        variance: 0,
         items: [],
+        kde: () => 0,
     };
-    const same = (a, b) => Math.abs(a - b) < eps;
     const valid = data.length - missing;
@@ -166,35 +237,3 @@ if (valid === 0) {
     }
-    const { median, q1, q3 } = quantiles(s, valid);
-    const iqr = q3 - q1;
-    const isCoefValid = typeof coef === 'number' && coef > 0;
-    let whiskerLow = isCoefValid ? Math.max(min, q1 - coef * iqr) : min;
-    let whiskerHigh = isCoefValid ? Math.min(max, q3 + coef * iqr) : max;
-    const outlier = [];
-    for (let i = 0; i < valid; ++i) {
-        const v = s[i];
-        if (v >= whiskerLow || same(v, whiskerLow)) {
-            if (whiskersMode === 'nearest') {
-                whiskerLow = v;
-            }
-            break;
-        }
-        if (outlier.length === 0 || !same(outlier[outlier.length - 1], v)) {
-            outlier.push(v);
-        }
-    }
-    const reversedOutliers = [];
-    for (let i = valid - 1; i >= 0; --i) {
-        const v = s[i];
-        if (v <= whiskerHigh || same(v, whiskerHigh)) {
-            if (whiskersMode === 'nearest') {
-                whiskerHigh = v;
-            }
-            break;
-        }
-        if ((reversedOutliers.length === 0 || !same(reversedOutliers[reversedOutliers.length - 1], v)) &&
-            (outlier.length === 0 || !same(outlier[outlier.length - 1], v))) {
-            reversedOutliers.push(v);
-        }
-    }
-    return {
+    const result = {
         min,
@@ -204,25 +243,13 @@ max,
         missing,
-        mean: sum / valid,
-        whiskerHigh,
-        whiskerLow,
-        outlier: outlier.concat(reversedOutliers.reverse()),
-        median,
-        q1,
-        q3,
-        iqr,
         items: s,
+        ...computeStats(s, valid),
+        ...computeWhiskers(s, valid, min, max, fullOptions),
     };
+    return {
+        ...result,
+        kde: kde(result),
+    };
 }
 
-exports.boxplot = boxplot;
-exports.default = boxplot;
-exports.quantilesFivenum = quantilesFivenum;
-exports.quantilesHigher = quantilesHigher;
-exports.quantilesHinges = quantilesHinges;
-exports.quantilesInterpolate = quantilesInterpolate;
-exports.quantilesLinear = quantilesLinear;
-exports.quantilesLower = quantilesLower;
-exports.quantilesMidpoint = quantilesMidpoint;
-exports.quantilesNearest = quantilesNearest;
-exports.quantilesType7 = quantilesType7;
+export { boxplot, boxplot as default, kde, quantilesFivenum, quantilesHigher, quantilesHinges, quantilesInterpolate, quantilesLinear, quantilesLower, quantilesMidpoint, quantilesNearest, quantilesType7 };
 //# sourceMappingURL=index.js.map

build/index.umd.min.js

@@ -1,2 +0,2 @@
-!function(e,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n((e="undefined"!=typeof globalThis?globalThis:e||self).BoxPlots={})}(this,(function(e){"use strict";function n(e,n,t){const r=n-1,i=n=>{const i=n*r,s=Math.floor(i),o=i-s,u=e[s];return 0===o?u:t(u,e[Math.min(s+1,r)],o)};return{q1:i(.25),median:i(.5),q3:i(.75)}}function t(e,t=e.length){return n(e,t,(e,n,t)=>e+t*(n-e))}function r(e,n=e.length){const t=n,r=Math.floor((t+3)/2)/2,i=n=>.5*(e[Math.floor(n)-1]+e[Math.ceil(n)-1]);return{q1:i(r),median:i((t+1)/2),q3:i(t+1-r)}}function i(e,n={}){const{quantiles:r,validAndSorted:i,coef:s,whiskersMode:o,eps:u}=Object.assign({coef:1.5,eps:.01,quantiles:t,validAndSorted:!1,whiskersMode:"nearest"},n),{missing:a,s:l,min:m,max:N,sum:c}=i?function(e){if(0===e.length)return{sum:Number.NaN,min:Number.NaN,max:Number.NaN,missing:0,s:[]};const n=e[0],t=e[e.length-1],r=(e,n)=>e+n;return{sum:(e instanceof Float32Array||Float64Array,e.reduce(r,0)),min:n,max:t,missing:0,s:e}}(e):function(e){let n=Number.POSITIVE_INFINITY,t=Number.NEGATIVE_INFINITY,r=0,i=0;const s=e.length,o=e instanceof Float64Array?new Float64Array(s):new Float32Array(s);for(let u=0;u<s;++u){const s=e[u];null==s||Number.isNaN(s)||(o[i]=s,i++,s<n&&(n=s),s>t&&(t=s),r+=s)}const u=s-i;if(0===i)return{sum:r,min:n,max:t,missing:u,s:[]};const a=i===s?o:o.subarray(0,i);return a.sort((e,n)=>e===n?0:e<n?-1:1),{sum:r,min:a[0],max:a[a.length-1],missing:u,s:a}}(e),f={min:Number.NaN,max:Number.NaN,mean:Number.NaN,missing:a,iqr:Number.NaN,count:e.length,whiskerHigh:Number.NaN,whiskerLow:Number.NaN,outlier:[],median:Number.NaN,q1:Number.NaN,q3:Number.NaN,items:[]},h=(e,n)=>Math.abs(e-n)<u,g=e.length-a;if(0===g)return f;const{median:d,q1:b,q3:q}=r(l,g),p=q-b,x="number"==typeof s&&s>0;let y=x?Math.max(m,b-s*p):m,M=x?Math.min(N,q+s*p):N;const w=[];for(let e=0;e<g;++e){const n=l[e];if(n>=y||h(n,y)){"nearest"===o&&(y=n);break}0!==w.length&&h(w[w.length-1],n)||w.push(n)}const I=[];for(let e=g-1;e>=0;--e){const n=l[e];if(n<=M||h(n,M)){"nearest"===o&&(M=n);break}0!==I.length&&h(I[I.length-1],n)||0!==w.length&&h(w[w.length-1],n)||I.push(n)}return{min:m,max:N,count:e.length,missing:a,mean:c/g,whiskerHigh:M,whiskerLow:y,outlier:w.concat(I.reverse()),median:d,q1:b,q3:q,iqr:p,items:l}}e.boxplot=i,e.default=i,e.quantilesFivenum=r,e.quantilesHigher=function(e,t=e.length){return n(e,t,(e,n)=>n)},e.quantilesHinges=function(e,n=e.length){return r(e,n)},e.quantilesInterpolate=n,e.quantilesLinear=function(e,t=e.length){return n(e,t,(e,n,t)=>e+(n-e)*t)},e.quantilesLower=function(e,t=e.length){return n(e,t,e=>e)},e.quantilesMidpoint=function(e,t=e.length){return n(e,t,(e,n)=>.5*(e+n))},e.quantilesNearest=function(e,t=e.length){return n(e,t,(e,n,t)=>t<.5?e:n)},e.quantilesType7=t,Object.defineProperty(e,"__esModule",{value:!0})}));
+!function(e,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n((e="undefined"!=typeof globalThis?globalThis:e||self).BoxPlots={})}(this,(function(e){"use strict";const n=Math.sqrt(2*Math.PI);function t(e){const t=e.items.length,r=function(e,n,t){let r=Math.sqrt(function(e,n){return e*n/(n-1)}(n,t));return"number"==typeof e&&(r=Math.min(r,e/1.34)),1.06*r*Math.pow(t,-.2)}(e.iqr,e.variance,t);return i=>{let o=0,s=0;for(o=0;o<t;o++){const t=e.items[o];s+=(a=(i-t)/r,Math.exp(-.5*a*a)/n)}var a;return s/r/t}}function r(e,n,t){const r=n-1,i=n=>{const i=n*r,o=Math.floor(i),s=i-o,a=e[o];return 0===s?a:t(a,e[Math.min(o+1,r)],s)};return{q1:i(.25),median:i(.5),q3:i(.75)}}function i(e,n=e.length){return r(e,n,((e,n,t)=>e+t*(n-e)))}function o(e,n=e.length){const t=n,r=Math.floor((t+3)/2)/2,i=n=>.5*(e[Math.floor(n)-1]+e[Math.ceil(n)-1]);return{q1:i(r),median:i((t+1)/2),q3:i(t+1-r)}}function s(e,n,t,r,{eps:i,quantiles:o,coef:s,whiskersMode:a}){const u=(e,n)=>Math.abs(e-n)<i,{median:l,q1:m,q3:c}=o(e,n),f=c-m,h="number"==typeof s&&s>0;let N=h?Math.max(t,m-s*f):t,g=h?Math.min(r,c+s*f):r;const d=[];for(let t=0;t<n;t+=1){const n=e[t];if(n>=N||u(n,N)){"nearest"===a&&(N=n);break}0!==d.length&&u(d[d.length-1],n)||d.push(n)}const q=[];for(let t=n-1;t>=0;t-=1){const n=e[t];if(n<=g||u(n,g)){"nearest"===a&&(g=n);break}0!==q.length&&u(q[q.length-1],n)||0!==d.length&&u(d[d.length-1],n)||q.push(n)}return{median:l,q1:m,q3:c,iqr:f,outlier:d.concat(q.reverse()),whiskerHigh:g,whiskerLow:N}}function a(e,n){let t=0;for(let r=0;r<n;r++){t+=e[r]}t/=n;let r=0;for(let i=0;i<n;i++){const n=e[i];r+=(n-t)*(n-t)}return r/=n,{mean:t,variance:r}}function u(e,n={}){const r={coef:1.5,eps:.01,quantiles:i,validAndSorted:!1,whiskersMode:"nearest",...n},{missing:o,s:u,min:l,max:m}=r.validAndSorted?function(e){return 0===e.length?{min:Number.NaN,max:Number.NaN,missing:0,s:[]}:{min:e[0],max:e[e.length-1],missing:0,s:e}}(e):function(e){let n=0;const{length:t}=e,r=e instanceof Float64Array?new Float64Array(t):new Float32Array(t);for(let i=0;i<t;i+=1){const t=e[i];null==t||Number.isNaN(t)||(r[n]=t,n+=1)}const i=t-n;if(0===n)return{min:Number.NaN,max:Number.NaN,missing:i,s:[]};const o=n===t?r:r.subarray(0,n);return o.sort(((e,n)=>e===n?0:e<n?-1:1)),{min:o[0],max:o[o.length-1],missing:i,s:o}}(e),c={min:Number.NaN,max:Number.NaN,mean:Number.NaN,missing:o,iqr:Number.NaN,count:e.length,whiskerHigh:Number.NaN,whiskerLow:Number.NaN,outlier:[],median:Number.NaN,q1:Number.NaN,q3:Number.NaN,variance:0,items:[],kde:()=>0},f=e.length-o;if(0===f)return c;const h={min:l,max:m,count:e.length,missing:o,items:u,...a(u,f),...s(u,f,l,m,r)};return{...h,kde:t(h)}}e.boxplot=u,e.default=u,e.kde=t,e.quantilesFivenum=o,e.quantilesHigher=function(e,n=e.length){return r(e,n,((e,n)=>n))},e.quantilesHinges=function(e,n=e.length){return o(e,n)},e.quantilesInterpolate=r,e.quantilesLinear=function(e,n=e.length){return r(e,n,((e,n,t)=>e+(n-e)*t))},e.quantilesLower=function(e,n=e.length){return r(e,n,(e=>e))},e.quantilesMidpoint=function(e,n=e.length){return r(e,n,((e,n)=>.5*(e+n)))},e.quantilesNearest=function(e,n=e.length){return r(e,n,((e,n,t)=>t<.5?e:n))},e.quantilesType7=i,Object.defineProperty(e,"__esModule",{value:!0})}));
 //# sourceMappingURL=index.umd.min.js.map

package.json

 {
   "name": "@sgratzl/boxplots",
   "description": "simple boxplot stats helper",
-  "version": "1.2.2",
+  "version": "1.3.0",
   "publishConfig": {
@@ -21,2 +21,3 @@ "access": "public"
     "whisker",
+    "kde",
     "quantiles"
@@ -29,4 +30,4 @@ ],
   "global": "BoxPlots",
-  "module": "build/index.esm.js",
-  "main": "build/index.js",
+  "module": "build/index.js",
+  "main": "build/index.cjs",
   "unpkg": "build/index.umd.min.js",
@@ -41,36 +42,38 @@ "jsdelivr": "build/index.umd.min.js",
   "devDependencies": {
-    "@rollup/plugin-commonjs": "^17.0.0",
-    "@rollup/plugin-node-resolve": "^11.0.0",
-    "@rollup/plugin-replace": "^2.3.4",
-    "@rollup/plugin-typescript": "^8.0.0",
-    "@types/jest": "^26.0.19",
-    "@typescript-eslint/eslint-plugin": "^4.9.1",
-    "@typescript-eslint/parser": "^4.9.1",
-    "@yarnpkg/pnpify": "^2.4.0",
-    "eslint": "^7.15.0",
-    "eslint-config-prettier": "^7.0.0",
+    "@babel/core": "^7.16.0",
+    "@babel/preset-env": "^7.16.0",
+    "@rollup/plugin-babel": "^5.3.0",
+    "@rollup/plugin-commonjs": "^21.0.1",
+    "@rollup/plugin-node-resolve": "^13.0.6",
+    "@rollup/plugin-replace": "^3.0.0",
+    "@rollup/plugin-typescript": "^8.3.0",
+    "@types/jest": "^27.0.2",
+    "@typescript-eslint/eslint-plugin": "^5.3.0",
+    "@typescript-eslint/parser": "^5.3.0",
+    "@yarnpkg/sdks": "^2.5.0",
+    "eslint": "^8.2.0",
+    "eslint-config-airbnb-typescript": "^15.0.0",
+    "eslint-config-prettier": "^8.3.0",
     "eslint-config-react-app": "^6.0.0",
-    "eslint-plugin-flowtype": "^5.2.0",
-    "eslint-plugin-import": "^2.22.1",
+    "eslint-plugin-flowtype": "^8.0.3",
+    "eslint-plugin-import": "^2.25.2",
     "eslint-plugin-jsx-a11y": "^6.4.1",
-    "eslint-plugin-prettier": "^3.2.0",
-    "eslint-plugin-react": "^7.21.5",
+    "eslint-plugin-prettier": "^4.0.0",
+    "eslint-plugin-react": "^7.26.1",
     "eslint-plugin-react-hooks": "^4.2.0",
-    "jest": "^26.6.3",
-    "prettier": "^2.2.1",
-    "release-it": "^14.2.2",
+    "jest": "^27.3.1",
+    "prettier": "^2.4.1",
     "rimraf": "^3.0.2",
-    "rollup": "^2.34.2",
-    "rollup-plugin-cleanup": "^3.2.1",
-    "rollup-plugin-dts": "^2.0.1",
+    "rollup": "^2.59.0",
+    "rollup-plugin-dts": "^4.0.1",
     "rollup-plugin-terser": "^7.0.2",
-    "ts-jest": "^26.4.4",
-    "tslib": "^2.0.3",
-    "typedoc": "^0.19.2",
-    "typescript": "^4.1.2"
+    "ts-jest": "^27.0.7",
+    "tslib": "^2.3.1",
+    "typedoc": "^0.22.8",
+    "typescript": "^4.4.4"
   },
   "scripts": {
-    "clean": "rimraf build node_modules \"*.tgz\"",
-    "compile": "tsc -p tsconfig.json --noEmit",
-    "start": "npm run watch",
+    "clean": "rimraf build docs node_modules \"*.tgz\" \"*.tsbuildinfo\"",
+    "compile": "tsc -b tsconfig.c.json",
+    "start": "yarn run watch",
     "watch": "rollup -c -w",
@@ -81,13 +84,12 @@ "build": "rollup -c",
     "test:coverage": "jest --passWithNoTests --coverage",
-    "lint": "npm run eslint && npm run prettier",
-    "fix": "npm run eslint:fix && npm run prettier:write",
-    "prettier:write": "prettier */** --write",
-    "prettier": "prettier */** --check",
+    "lint": "yarn run eslint && yarn run prettier",
+    "fix": "yarn run eslint:fix && yarn run prettier:write",
+    "prettier:write": "prettier \"*\" \"*/**\" --write",
+    "prettier": "prettier \"*\" \"*/**\" --check",
     "eslint": "eslint src --ext .ts,.tsx",
-    "eslint:fix": "npm run eslint -- --fix",
-    "docs": "typedoc",
-    "prepare": "npm run build",
-    "release": "release-it --disable-metrics --npm.skipChecks",
-    "release:pre": "release-it --disable-metrics --npm.skipChecks --preRelease=alpha --npm.tag=next"
-  }
+    "eslint:fix": "yarn run eslint --fix",
+    "docs": "typedoc src/index.ts",
+    "prepare": "yarn run build"
+  },
+  "packageManager": "yarn@3.1.0"
 }

README.md

@@ -19,12 +19,3 @@ # BoxPlots
 const arr = [
-  -0.402253,
-  -1.4521869,
-  0.135228,
-  -1.8620118,
-  -0.5687531,
-  0.4218371,
-  -1.1165662,
-  0.5960255,
-  -0.5008038,
-  -0.394178,
+  -0.402253, -1.4521869, 0.135228, -1.8620118, -0.5687531, 0.4218371, -1.1165662, 0.5960255, -0.5008038, -0.394178,
   1.3709885,
@@ -31,0 +22,0 @@ ];

src/boxplot.spec.ts

@@ -11,12 +11,3 @@ /// <reference types="jest" />
     const arr = [
-      -0.402253,
-      -1.4521869,
-      0.135228,
-      -1.8620118,
-      -0.5687531,
-      0.4218371,
-      -1.1165662,
-      0.5960255,
-      -0.5008038,
-      -0.394178,
+      -0.402253, -1.4521869, 0.135228, -1.8620118, -0.5687531, 0.4218371, -1.1165662, 0.5960255, -0.5008038, -0.394178,
       1.3709885,
@@ -23,0 +14,0 @@ ];

src/boxplot.ts

@@ -0,1 +1,2 @@
+import kde, { KernelDensityEstimator } from './kde';
 import { quantilesType7 } from './quantiles';
@@ -50,2 +51,7 @@
   /**
+   * variance
+   */
+  readonly variance: number;
+
+  /**
    * number of missing values (NaN, null, undefined) in the data
@@ -62,2 +68,4 @@ */
   readonly items: ArrayLike<number>;
+
+  readonly kde: KernelDensityEstimator;
 }
@@ -105,25 +113,15 @@
 function createSortedData(data: readonly number[] | Float32Array | Float64Array) {
-  let min = Number.POSITIVE_INFINITY;
-  let max = Number.NEGATIVE_INFINITY;
-  let sum = 0;
   let valid = 0;
-  const length = data.length;
+  const { length } = data;
 
   const vs = data instanceof Float64Array ? new Float64Array(length) : new Float32Array(length);
 
-  for (let i = 0; i < length; ++i) {
+  for (let i = 0; i < length; i += 1) {
     const v = data[i];
     if (v == null || Number.isNaN(v)) {
+      // eslint-disable-next-line no-continue
       continue;
     }
     vs[valid] = v;
-    valid++;
-
-    if (v < min) {
-      min = v;
-    }
-    if (v > max) {
-      max = v;
-    }
-    sum += v;
+    valid += 1;
   }
@@ -135,5 +133,4 @@
     return {
-      sum,
-      min,
-      max,
+      min: Number.NaN,
+      max: Number.NaN,
       missing,
@@ -145,13 +142,16 @@ s: [],
   // add comparator since the polyfill doesn't to a real sorting
-  const s = valid === length ? vs : vs.subarray(0, valid);
+  const validData = valid === length ? vs : vs.subarray(0, valid);
   // sort in place
-  s.sort((a, b) => (a === b ? 0 : a < b ? -1 : 1));
+  // eslint-disable-next-line no-nested-ternary
+  validData.sort((a, b) => (a === b ? 0 : a < b ? -1 : 1));
 
   // use real number for better precision
+  const min = validData[0];
+  const max = validData[validData.length - 1];
+
   return {
-    sum,
-    min: s[0],
-    max: s[s.length - 1],
+    min,
+    max,
     missing,
-    s,
+    s: validData,
   };
@@ -163,3 +163,2 @@ }
     return {
-      sum: Number.NaN,
       min: Number.NaN,
@@ -173,12 +172,4 @@ max: Number.NaN,
   const max = data[data.length - 1];
-  const red = (acc: number, v: number) => acc + v;
-  const sum =
-    data instanceof Float32Array
-      ? data.reduce(red, 0)
-      : data instanceof Float64Array
-      ? data.reduce(red, 0)
-      : data.reduce(red, 0);
 
   return {
-    sum,
     min,
@@ -191,52 +182,21 @@ max,
 
-export default function boxplot(
-  data: readonly number[] | Float32Array | Float64Array,
-  options: BoxplotStatsOptions = {}
-): IBoxPlot {
-  const { quantiles, validAndSorted, coef, whiskersMode, eps }: Required<BoxplotStatsOptions> = Object.assign(
-    {
-      coef: 1.5,
-      eps: 10e-3,
-      quantiles: quantilesType7,
-      validAndSorted: false,
-      whiskersMode: 'nearest',
-    },
-    options
-  );
-
-  const { missing, s, min, max, sum } = validAndSorted ? withSortedData(data) : createSortedData(data);
-
-  const invalid = {
-    min: Number.NaN,
-    max: Number.NaN,
-    mean: Number.NaN,
-    missing,
-    iqr: Number.NaN,
-    count: data.length,
-    whiskerHigh: Number.NaN,
-    whiskerLow: Number.NaN,
-    outlier: [],
-    median: Number.NaN,
-    q1: Number.NaN,
-    q3: Number.NaN,
-    items: [],
-  };
-
+function computeWhiskers(
+  s: ArrayLike<number>,
+  valid: number,
+  min: number,
+  max: number,
+  { eps, quantiles, coef, whiskersMode }: Required<BoxplotStatsOptions>
+) {
   const same = (a: number, b: number) => Math.abs(a - b) < eps;
 
-  const valid = data.length - missing;
-
-  if (valid === 0) {
-    return invalid;
-  }
-
   const { median, q1, q3 } = quantiles(s, valid);
   const iqr = q3 - q1;
   const isCoefValid = typeof coef === 'number' && coef > 0;
+
   let whiskerLow = isCoefValid ? Math.max(min, q1 - coef * iqr) : min;
   let whiskerHigh = isCoefValid ? Math.min(max, q3 + coef * iqr) : max;
 
-  const outlier: number[] = [];
+  const outlierLow: number[] = [];
   // look for the closest value which is bigger than the computed left
-  for (let i = 0; i < valid; ++i) {
+  for (let i = 0; i < valid; i += 1) {
     const v = s[i];
@@ -250,9 +210,9 @@ if (v >= whiskerLow || same(v, whiskerLow)) {
     // outlier
-    if (outlier.length === 0 || !same(outlier[outlier.length - 1], v)) {
-      outlier.push(v);
+    if (outlierLow.length === 0 || !same(outlierLow[outlierLow.length - 1], v)) {
+      outlierLow.push(v);
     }
   }
   // look for the closest value which is smaller than the computed right
-  const reversedOutliers: number[] = [];
-  for (let i = valid - 1; i >= 0; --i) {
+  const reversedOutlierHigh: number[] = [];
+  for (let i = valid - 1; i >= 0; i -= 1) {
     const v = s[i];
@@ -267,10 +227,81 @@ if (v <= whiskerHigh || same(v, whiskerHigh)) {
     if (
-      (reversedOutliers.length === 0 || !same(reversedOutliers[reversedOutliers.length - 1], v)) &&
-      (outlier.length === 0 || !same(outlier[outlier.length - 1], v))
+      (reversedOutlierHigh.length === 0 || !same(reversedOutlierHigh[reversedOutlierHigh.length - 1], v)) &&
+      (outlierLow.length === 0 || !same(outlierLow[outlierLow.length - 1], v))
     ) {
-      reversedOutliers.push(v);
+      reversedOutlierHigh.push(v);
     }
   }
+  const outlier = outlierLow.concat(reversedOutlierHigh.reverse());
 
   return {
+    median,
+    q1,
+    q3,
+    iqr,
+    outlier,
+    whiskerHigh,
+    whiskerLow,
+  };
+}
+
+function computeStats(s: ArrayLike<number>, valid: number) {
+  let mean = 0;
+
+  for (let i = 0; i < valid; i++) {
+    const v = s[i];
+    mean += v;
+  }
+  mean /= valid;
+
+  let variance = 0;
+  for (let i = 0; i < valid; i++) {
+    const v = s[i];
+    variance += (v - mean) * (v - mean);
+  }
+  variance /= valid;
+
+  return {
+    mean,
+    variance,
+  };
+}
+
+export default function boxplot(
+  data: readonly number[] | Float32Array | Float64Array,
+  options: BoxplotStatsOptions = {}
+): IBoxPlot {
+  const fullOptions: Required<BoxplotStatsOptions> = {
+    coef: 1.5,
+    eps: 10e-3,
+    quantiles: quantilesType7,
+    validAndSorted: false,
+    whiskersMode: 'nearest',
+    ...options,
+  };
+
+  const { missing, s, min, max } = fullOptions.validAndSorted ? withSortedData(data) : createSortedData(data);
+
+  const invalid: IBoxPlot = {
+    min: Number.NaN,
+    max: Number.NaN,
+    mean: Number.NaN,
+    missing,
+    iqr: Number.NaN,
+    count: data.length,
+    whiskerHigh: Number.NaN,
+    whiskerLow: Number.NaN,
+    outlier: [],
+    median: Number.NaN,
+    q1: Number.NaN,
+    q3: Number.NaN,
+    variance: 0,
+    items: [],
+    kde: () => 0,
+  };
+  const valid = data.length - missing;
+
+  if (valid === 0) {
+    return invalid;
+  }
+  const result: Omit<IBoxPlot, 'kde'> = {
     min,
@@ -280,12 +311,10 @@ max,
     missing,
-    mean: sum / valid,
-    whiskerHigh,
-    whiskerLow,
-    outlier: outlier.concat(reversedOutliers.reverse()),
-    median,
-    q1,
-    q3,
-    iqr,
     items: s,
+    ...computeStats(s, valid),
+    ...computeWhiskers(s, valid, min, max, fullOptions),
   };
+  return {
+    ...result,
+    kde: kde(result),
+  };
 }

src/index.ts

 export * from './boxplot';
 export { default as boxplot, default } from './boxplot';
+export { default as kde, KernelDensityEstimator } from './kde';
 export * from './quantiles';

src/quantiles.spec.ts

@@ -20,12 +20,3 @@ /// <reference types="jest" />
     const arr = [
-      -0.402253,
-      -1.4521869,
-      0.135228,
-      -1.8620118,
-      -0.5687531,
-      0.4218371,
-      -1.1165662,
-      0.5960255,
-      -0.5008038,
-      -0.394178,
+      -0.402253, -1.4521869, 0.135228, -1.8620118, -0.5687531, 0.4218371, -1.1165662, 0.5960255, -0.5008038, -0.394178,
       1.3709885,
@@ -48,14 +39,4 @@ ].sort(asc);
     const arr = [
-      1.086657167,
-      0.294672807,
-      1.462293013,
-      0.485641706,
-      1.57748264,
-      0.827809286,
-      -0.397192557,
-      -1.222111542,
-      1.071236583,
-      -1.182959319,
-      -0.003749222,
-      -0.360759239,
+      1.086657167, 0.294672807, 1.462293013, 0.485641706, 1.57748264, 0.827809286, -0.397192557, -1.222111542,
+      1.071236583, -1.182959319, -0.003749222, -0.360759239,
     ].sort(asc);
@@ -62,0 +43,0 @@ it('type7', () => {

src/quantiles.ts

@@ -0,1 +1,7 @@
+export interface QuantilesResult {
+  q1: number;
+  median: number;
+  q3: number;
+}
+
 /**
@@ -10,3 +16,3 @@ * computes the boxplot stats using the given interpolation function if needed
   interpolate: (i: number, j: number, fraction: number) => number
-) {
+): QuantilesResult {
   const n1 = length - 1;
@@ -33,3 +39,3 @@ const compute = (q: number) => {
  */
-export function quantilesType7(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesType7(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesInterpolate(arr, length, (a, b, alpha) => a + alpha * (b - a));
@@ -42,3 +48,3 @@ }
  */
-export function quantilesLinear(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesLinear(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesInterpolate(arr, length, (i, j, fraction) => i + (j - i) * fraction);
@@ -50,3 +56,3 @@ }
  */
-export function quantilesLower(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesLower(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesInterpolate(arr, length, (i) => i);
@@ -58,3 +64,3 @@ }
  */
-export function quantilesHigher(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesHigher(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesInterpolate(arr, length, (_, j) => j);
@@ -66,3 +72,3 @@ }
  */
-export function quantilesNearest(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesNearest(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesInterpolate(arr, length, (i, j, fraction) => (fraction < 0.5 ? i : j));
@@ -74,3 +80,3 @@ }
  */
-export function quantilesMidpoint(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesMidpoint(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesInterpolate(arr, length, (i, j) => (i + j) * 0.5);
@@ -86,3 +92,3 @@ }
  */
-export function quantilesFivenum(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesFivenum(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   // based on R fivenum
@@ -107,4 +113,4 @@ const n = length;
  */
-export function quantilesHinges(arr: ArrayLike<number>, length = arr.length) {
+export function quantilesHinges(arr: ArrayLike<number>, length = arr.length): QuantilesResult {
   return quantilesFivenum(arr, length);
 }

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