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Comparing version 7.6.0 to 7.7.0

src/sample_rank_correlation.d.ts

8

CHANGELOG.md

@@ -5,2 +5,10 @@ # Changelog

## [7.7.0](https://github.com/simple-statistics/simple-statistics/compare/v7.6.0...v7.7.0) (2021-03-31)
### Features
* Implement Wilcoxon rank sum statistic ([#568](https://github.com/simple-statistics/simple-statistics/issues/568)) ([77cbec2](https://github.com/simple-statistics/simple-statistics/commit/77cbec21b2f0dd6cb2e47c244859612b4a8f2064))
* Rank correlation ([#562](https://github.com/simple-statistics/simple-statistics/issues/562)) ([4244faf](https://github.com/simple-statistics/simple-statistics/commit/4244fafd81dd25e49c1d848432bfa15109c1cab0))
## [7.6.0](https://github.com/simple-statistics/simple-statistics/compare/v7.5.0...v7.6.0) (2021-03-02)

@@ -7,0 +15,0 @@

2

dist/simple-statistics.min.js

@@ -1,2 +0,2 @@

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r.length/t},r.interquartileRange=d,r.inverseErrorFunction=Z,r.iqr=d,r.kMeansCluster=function(r,t,n){void 0===n&&(n=Math.random);for(var e=null,a=x(r,t,n),o=null,i=Number.MAX_VALUE;0!==i;)e=a,i=er(a=nr(r,o=tr(r,a),t),e);return{labels:o,centroids:a}},r.kde=G,r.kernelDensityEstimation=G,r.linearRegression=function(r){var t,n,e=r.length;if(1===e)t=0,n=r[0][1];else{for(var a,o,i,u=0,h=0,f=0,l=0,s=0;s<e;s++)u+=o=(a=r[s])[0],h+=i=a[1],f+=o*o,l+=o*i;n=h/e-(t=(e*l-u*h)/(e*f-u*u))*u/e}return{m:t,b:n}},r.linearRegressionLine=function(r){return function(t){return r.b+r.m*t}},r.logAverage=function(r){if(0===r.length)throw new Error("logAverage requires at least one data point");for(var t=0,n=0;n<r.length;n++){if(r[n]<0)throw new Error("logAverage requires only non-negative numbers as input");t+=Math.log(r[n])}return Math.exp(t/r.length)},r.logit=function(r){if(r<=0||r>=1)throw new Error("p must be strictly between zero and one");return Math.log(r/(1-r))},r.mad=q,r.max=f,r.maxSorted=function(r){return r[r.length-1]},r.mean=n,r.meanSimple=D,r.median=b,r.medianAbsoluteDeviation=q,r.medianSorted=function(r){return s(r,.5)},r.min=h,r.minSorted=function(r){return r[0]},r.mode=function(r){return i(u(r))},r.modeFast=function(r){for(var t,n=new Map,e=0,a=0;a<r.length;a++){var o=n.get(r[a]);void 0===o?o=1:o++,o>e&&(t=r[a],e=o),n.set(r[a],o)}if(0===e)throw new Error("mode requires at last one data point");return t},r.modeSorted=i,r.numericSort=u,r.perceptron=F,r.permutationTest=function(r,t,e,a,o){if(void 0===a&&(a=1e4),void 0===e&&(e="two_side"),"two_side"!==e&&"greater"!==e&&"less"!==e)throw new Error("`alternative` must be either 'two_side', 'greater', or 'less'");for(var i=n(r)-n(t),u=new Array(a),h=r.concat(t),f=Math.floor(h.length/2),l=0;l<a;l++){E(h,o);var s=h.slice(0,f),c=h.slice(f,h.length),g=n(s)-n(c);u[l]=g}var v=0;if("two_side"===e)for(var p=0;p<=a;p++)Math.abs(u[p])>=Math.abs(i)&&(v+=1);else if("greater"===e)for(var m=0;m<=a;m++)u[m]>=i&&(v+=1);else for(var w=0;w<=a;w++)u[w]<=i&&(v+=1);return v/a},r.permutationsHeap=function(r){for(var t=new Array(r.length),n=[r.slice()],e=0;e<r.length;e++)t[e]=0;for(var a=0;a<r.length;)if(t[a]<a){var o=0;a%2!=0&&(o=t[a]);var i=r[o];r[o]=r[a],r[a]=i,n.push(r.slice()),t[a]++,a=0}else t[a]=0,a++;return n},r.poissonDistribution=function(r){if(!(r<=0)){var t=0,n=0,e=[],a=1;do{e[t]=Math.exp(-r)*Math.pow(r,t)/a,n+=e[t],a*=++t}while(n<.9999);return e}},r.probit=function(r){return 0===r?r=L:r>=1&&(r=.9999),Math.sqrt(2)*Z(2*r-1)},r.product=function(r){for(var t=1,n=0;n<r.length;n++)t*=r[n];return t},r.quantile=v,r.quantileRank=function(r,t){return M(u(r),t)},r.quantileRankSorted=M,r.quantileSorted=s,r.quickselect=c,r.rSquared=function(r,t){if(r.length<2)return 1;for(var n=0,e=0;e<r.length;e++)n+=r[e][1];for(var a=n/r.length,o=0,i=0;i<r.length;i++)o+=Math.pow(a-r[i][1],2);for(var u=0,h=0;h<r.length;h++)u+=Math.pow(r[h][1]-t(r[h][0]),2);return 1-u/o},r.relativeError=ur,r.rms=R,r.rootMeanSquare=R,r.sample=x,r.sampleCorrelation=T,r.sampleCovariance=I,r.sampleKurtosis=function(r){var t=r.length;if(t<4)throw new Error("sampleKurtosis requires at least four data points");for(var e,a=n(r),o=0,i=0,u=0;u<t;u++)o+=(e=r[u]-a)*e,i+=e*e*e*e;return(t-1)/((t-2)*(t-3))*(t*(t+1)*i/(o*o)-3*(t-1))},r.sampleRankCorrelation=function(r,t){for(var n=r.map((function(r,t){return[r,t]})).sort((function(r,t){return r[0]-t[0]})).map((function(r){return r[1]})),e=t.map((function(r,t){return[r,t]})).sort((function(r,t){return r[0]-t[0]})).map((function(r){return r[1]})),a=Array(n.length),o=Array(n.length),i=0;i<n.length;i++)a[n[i]]=i,o[e[i]]=i;return T(a,o)},r.sampleSkewness=function(r){if(r.length<3)throw new Error("sampleSkewness requires at least three data points");for(var t,e=n(r),a=0,o=0,i=0;i<r.length;i++)a+=(t=r[i]-e)*t,o+=t*t*t;var u=r.length-1,h=Math.sqrt(a/u),f=r.length;return f*o/((f-1)*(f-2)*Math.pow(h,3))},r.sampleStandardDeviation=N,r.sampleVariance=C,r.sampleWithReplacement=function(r,t,n){if(0===r.length)return[];n=n||Math.random;for(var e=r.length,a=[],o=0;o<t;o++){var i=Math.floor(n()*e);a.push(r[i])}return a},r.shuffle=y,r.shuffleInPlace=E,r.sign=$,r.silhouette=ar,r.silhouetteMetric=function(r,t){return f(ar(r,t))},r.standardDeviation=o,r.standardNormalTable=J,r.subtractFromMean=function(r,t,n){return(r*t-n)/(t-1)},r.sum=t,r.sumNthPowerDeviations=e,r.sumSimple=l,r.tTest=function(r,t){return(n(r)-t)/(o(r)/Math.sqrt(r.length))},r.tTestTwoSample=function(r,t,e){var a=r.length,o=t.length;if(!a||!o)return null;e||(e=0);var i=n(r),u=n(t),h=C(r),f=C(t);if("number"==typeof i&&"number"==typeof u&&"number"==typeof h&&"number"==typeof f){var l=((a-1)*h+(o-1)*f)/(a+o-2);return(i-u-e)/Math.sqrt(l*(1/a+1/o))}},r.uniqueCountSorted=k,r.variance=a,r.wilcoxonRankSum=function(r,t){if(!r.length||!t.length)throw new Error("Neither sample can be empty");for(var n=r.map((function(r){return{label:"x",value:r}})).concat(t.map((function(r){return{label:"y",value:r}}))).sort((function(r,t){return r.value-t.value})),e=0;e<n.length;e++)n[e].rank=e;for(var a=[n[0].rank],o=1;o<n.length;o++)n[o].value===n[o-1].value?(a.push(n[o].rank),o===n.length-1&&i(n,a)):a.length>1?i(n,a):a=[n[o].rank];function i(r,t){for(var n=(t[0]+t[t.length-1])/2,e=0;e<t.length;e++)r[t[e]].rank=n}for(var u=0,h=0;h<n.length;h++){var f=n[h];"x"===f.label&&(u+=f.rank+1)}return u},r.zScore=function(r,t,n){return(r-t)/n},Object.defineProperty(r,"__esModule",{value:!0})}));
//# sourceMappingURL=simple-statistics.min.js.map

@@ -47,2 +47,3 @@ // # simple-statistics

export { default as sampleCorrelation } from "./src/sample_correlation";
export { default as sampleRankCorrelation } from "./src/sample_rank_correlation";
export { default as sampleVariance } from "./src/sample_variance";

@@ -84,2 +85,3 @@ export { default as sampleStandardDeviation } from "./src/sample_standard_deviation";

export { default as tTestTwoSample } from "./src/t_test_two_sample";
export { default as wilcoxonRankSum } from "./src/wilcoxon_rank_sum";
// ss.jenks = require('./src/jenks');

@@ -86,0 +88,0 @@

{
"name": "simple-statistics",
"version": "7.6.0",
"version": "7.7.0",
"description": "Simple Statistics",

@@ -17,14 +17,14 @@ "author": "Tom MacWright <tom@macwright.org> (http://macwright.org/)",

"devDependencies": {
"@rollup/plugin-buble": "^0.21.1",
"cz-conventional-changelog": "^3.0.2",
"@rollup/plugin-buble": "^0.21.3",
"cz-conventional-changelog": "^3.3.0",
"documentation": "^13",
"eslint": "^7.2.0",
"eslint-plugin-prettier": "^3.0.0",
"prettier": "^2.0.2",
"random-js": "^2.0.0",
"rollup": "^2.3.2",
"rollup-plugin-terser": "^7.0.1",
"standard-version": "^9.0.0",
"tap": "^14.4.1",
"typescript": "^4.0.2"
"eslint": "^7.23.0",
"eslint-plugin-prettier": "^3.3.1",
"prettier": "^2.2.1",
"random-js": "^2.1.0",
"rollup": "^2.44.0",
"rollup-plugin-terser": "^7.0.2",
"standard-version": "^9.1.1",
"tap": "^14.11.0",
"typescript": "^4.2.3"
},

@@ -37,3 +37,3 @@ "scripts": {

"prelint": "tsc --skipLibCheck",
"lint": "eslint index.js src test && prettier --check src/*.d.ts",
"lint": "eslint index.js src test && prettier --check src test \"!test/types.js\"",
"postlint": "documentation lint src"

@@ -40,0 +40,0 @@ },

@@ -41,3 +41,3 @@ # Simple Statistics

```HTML
<script src='https://unpkg.com/simple-statistics@7.6.0/dist/simple-statistics.min.js'>
<script src='https://unpkg.com/simple-statistics@7.7.0/dist/simple-statistics.min.js'>
</script>

@@ -51,3 +51,3 @@ ```

<script type='module'>
import {min} from "https://unpkg.com/simple-statistics@7.6.0/index.js?module"
import {min} from "https://unpkg.com/simple-statistics@7.7.0/index.js?module"
console.log(min([1, 2, 3]))

@@ -54,0 +54,0 @@ </script>

@@ -8,3 +8,3 @@ /**

* @returns {Array<number>} minimum & maximum value
* @throws {Error} if the the length of x is less than one
* @throws {Error} if the length of x is less than one
* @example

@@ -11,0 +11,0 @@ * extent([1, 2, 3, 4]);

@@ -37,3 +37,3 @@ /**

// divide n by the the reciprocal sum
// divide n by the reciprocal sum
return x.length / reciprocalSum;

@@ -40,0 +40,0 @@ }

@@ -8,3 +8,3 @@ /**

* @returns {number} maximum value
* @throws {Error} if the the length of x is less than one
* @throws {Error} if the length of x is less than one
* @example

@@ -11,0 +11,0 @@ * max([1, 2, 3, 4]);

@@ -18,3 +18,3 @@ import sumSimple from "./sum_simple";

* @param {Array<number>} x sample of one or more data points
* @throws {Error} if the the length of x is less than one
* @throws {Error} if the length of x is less than one
* @returns {number} mean

@@ -21,0 +21,0 @@ * @example

@@ -12,3 +12,3 @@ import sum from "./sum";

* @param {Array<number>} x sample of one or more data points
* @throws {Error} if the the length of x is less than one
* @throws {Error} if the length of x is less than one
* @returns {number} mean

@@ -15,0 +15,0 @@ * @example

@@ -6,3 +6,3 @@ /**

* @param {Array<number>} x sample of one or more data points
* @throws {Error} if the the length of x is less than one
* @throws {Error} if the length of x is less than one
* @returns {number} minimum value

@@ -9,0 +9,0 @@ * @example

@@ -21,11 +21,6 @@ const SQRT_2PI = Math.sqrt(2 * Math.PI);

* is a mathematical table for the values of Φ (phi), which are the values of
* the cumulative distribution function of the normal distribution.
* It is used to find the probability that a statistic is observed below,
* above, or between values on the standard normal distribution, and by
* extension, any normal distribution.
*
* The probabilities are calculated using the
* [Cumulative distribution function](https://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution_function).
* The table used is the cumulative, and not cumulative from 0 to mean
* (even though the latter has 5 digits precision, instead of 4).
* the [cumulative distribution function](https://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution_function)
* of the normal distribution. It is used to find the probability that a
* statistic is observed below, above, or between values on the standard
* normal distribution, and by extension, any normal distribution.
*/

@@ -32,0 +27,0 @@ const standardNormalTable = [];

@@ -19,3 +19,3 @@ import mean from "./mean";

*
* [This is used to confirm or deny](http://www.monarchlab.org/Lab/Research/Stats/2SampleT.aspx)
* [This is used to reject](https://en.wikipedia.org/wiki/Exclusion_of_the_null_hypothesis)
* a null hypothesis that the two populations that have been sampled into

@@ -22,0 +22,0 @@ * `sampleX` and `sampleY` are equal to each other.

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