feat: initial commit

README.md

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+## About
+
+![](decision-method.png)
+
+This package contains a series of utilities related to forecasting. It is currently in _alpha_. So far, it only contains utilities related to forecast aggregation, but I may add content related to scoring, charts, etc.
+
+## Built with
+
+- vanilla javascript
+- [Best readme template](https://github.com/othneildrew/Best-README-Template)
+
+## Getting started
+
+### Installation
+
+```sh
+npm install forecasting
+```
+
+### Usage
+
+#### Aggregation
+
+```js
+import {
+  median,
+  arithmeticMean,
+  geometricMean,
+  geometricMeanOfOdds,
+  extremizedGeometricMeanOfOdds,
+  neyman,
+} from "forecasting";
+
+let ps = [0.1, 0.2, 0.4, 0.5];
+console.log(ps);
+
+console.log(median(ps));
+console.log(arithmeticMean(ps));
+console.log(geometricMean(ps));
+console.log(geometricMeanOfOdds(ps));
+console.log(extremizedGeometricMeanOfOdds(ps, 1.5)); // 1.5 is the extremization factor
+console.log(extremizedGeometricMeanOfOdds(ps, 2.5));
+console.log(neyman(ps));
+```
+
+You may also install [@forecasting/aggregation](https://www.npmjs.com/package/@forecasting/aggregation) directly
+
+#### Scoring
+
+To be done
+
+#### Charts
+
+To be done
+
+## Roadmap
+
+- [ ] Do another repository for scoring methods
+- [ ] Do another repository for charts

index.js

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+import {
+  median,
+  arithmeticMean,
+  geometricMean,
+  geometricMeanOfOdds,
+  extremizedGeometricMeanOfOdds,
+  neyman,
+} from "@forecasting/aggregation";
+
+export {
+  median,
+  arithmeticMean,
+  geometricMean,
+  geometricMeanOfOdds,
+  extremizedGeometricMeanOfOdds,
+  neyman,
+};

node_modules/.package-lock.json

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+{
+  "name": "forecasting",
+  "version": "0.0.1",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {
+    "node_modules/@forecasting/aggregation": {
+      "version": "0.0.1",
+      "resolved": "https://registry.npmjs.org/@forecasting/aggregation/-/aggregation-0.0.1.tgz",
+      "integrity": "sha512-N6NwJaHioyJQZwjvbNYQCknegrQoWne5I1TILA0jSu+8xkCIN+16cYumc1hZSYAKTzfBsiQWXZbuubfVMpgXFg=="
+    }
+  }
+}

node_modules/@forecasting/aggregation/README.md

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+## About
+
+![](decision-method.png)
+
+This package contains a series of utilities for forecast aggregation. It is currently in _alpha_, meaning that the code itself works, but there isn't error checking.
+
+For an introduction to different aggregation methods, see Jaime Sevilla's [Aggregation](https://forum.effectivealtruism.org/s/hjiBqAJNKhfJFq7kf) series. For an explanation of the neyman method, see [here](https://forum.effectivealtruism.org/s/hjiBqAJNKhfJFq7kf/p/biL94PKfeHmgHY6qe).
+
+## Built with
+
+- vanilla javascript
+- [Best readme template](https://github.com/othneildrew/Best-README-Template)
+
+## Getting started
+
+### Installation
+
+```sh
+npm install @forecasting/aggregation
+```
+
+### Usage
+
+```js
+import {
+  median,
+  arithmeticMean,
+  geometricMean,
+  geometricMeanOfOdds,
+  extremizedGeometricMeanOfOdds,
+  neyman,
+} from "@forecasting/aggregation";
+
+let ps = [0.1, 0.2, 0.4, 0.5];
+console.log(ps);
+
+console.log(median(ps));
+console.log(arithmeticMean(ps));
+console.log(geometricMean(ps));
+console.log(geometricMeanOfOdds(ps));
+console.log(extremizedGeometricMeanOfOdds(ps, 1.5)); // 1.5 is the extremization factor
+console.log(extremizedGeometricMeanOfOdds(ps, 2.5));
+console.log(neyman(ps));
+```
+
+## Roadmap
+
+- [ ] validate probabilities (must be 0<= p <=1)
+- [ ] Decide on a return type if probabilities are not validated (-1? / null?)
+- [ ] Write wrapper code for validation
+- [ ] Validate that array.length > 0
+- [ ] add weighting? by recency?
+- [ ] filter outliers?
+- [ ] Write documentation
+- [ ] Do another repository for scoring methods

node_modules/@forecasting/aggregation/index.js

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+// Helpers
+const sum = (array) => array.reduce((a, b) => a + b, 0);
+const probabilityToOdds = (p) => p / (1 - p);
+const oddsToProbability = (o) => o / (1 + o);
+
+// Main functions
+export const median = (array) => {
+  // needs validation array not empty
+  let midway = Math.floor(array.length);
+  let arrayToBeSorted = [...array];
+  // sorting mutates the array, which I am averse to
+  let arraySorted = arrayToBeSorted.sort((a, b) => a - b);
+  if (midway % 2) {
+    return arraySorted[midway];
+  } else {
+    return (arraySorted[midway - 1] + arraySorted[midway]) / 2;
+  }
+};
+
+export const arithmeticMean = (array) => {
+  let result = sum(array) / array.length;
+  return result;
+};
+
+export const geometricMean = (array) => {
+  // sum of logs seems more numerically stable than multiplying a lot of numbers 0<=p<=1
+  let arrayAsLog = array.map((p) => Math.log(p));
+  let sumOfLogs = sum(arrayAsLog) / arrayAsLog.length;
+  let result = Math.exp(sumOfLogs);
+  return result;
+};
+
+export const geometricMeanOfOdds = (array) => {
+  let arrayOfOdds = array.map((p) => probabilityToOdds(p));
+  let arrayOfLogsOfOdds = arrayOfOdds.map((p) => Math.log(p));
+  let sumOfLogsOfOdds = sum(arrayOfLogsOfOdds) / arrayOfLogsOfOdds.length;
+  let geomMeanOfOdds = Math.exp(sumOfLogsOfOdds);
+  let result = oddsToProbability(geomMeanOfOdds);
+  return result;
+};
+
+export const extremizedGeometricMeanOfOdds = (
+  array,
+  extremizationParameter = 1.5
+) => {
+  let arrayOfOdds = array.map((p) => probabilityToOdds(p));
+  let arrayOfLogsOfOdds = arrayOfOdds.map((p) => Math.log(p));
+  let extremizedSumOfLogsOfOdds =
+    (extremizationParameter * sum(arrayOfLogsOfOdds)) /
+    arrayOfLogsOfOdds.length;
+  let extremizedGeomMeanOfOdds = Math.exp(extremizedSumOfLogsOfOdds);
+  let result = oddsToProbability(extremizedGeomMeanOfOdds);
+  return result;
+};
+
+export const neyman = (array) => {
+  let n = array.length;
+
+  let d =
+    (n * (Math.sqrt(3 * Math.pow(n, 2) - 3 * n + 1) - 2)) /
+    (Math.pow(n, 2) - n - 1);
+  let result = extremizedGeometricMeanOfOdds(array, d);
+  return result;
+};

node_modules/@forecasting/aggregation/package.json

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+{
+  "name": "@forecasting/aggregation",
+  "version": "0.0.1",
+  "description": "Forecasting aggregation utilities",
+  "main": "index.js",
+  "scripts": {
+    "test": "node test.js"
+  },
+  "keywords": [
+    "forecasting",
+    "aggregation",
+    "prediction",
+    "prediction",
+    "markets"
+  ],
+  "author": "Nuño Sempere",
+  "license": "MIT"
+}

node_modules/@forecasting/aggregation/tests.js

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+import {
+  median,
+  arithmeticMean,
+  geometricMean,
+  geometricMeanOfOdds,
+  extremizedGeometricMeanOfOdds,
+  neyman,
+} from "./index.js";
+
+let ps = [0.1, 0.2, 0.4, 0.5];
+console.log(ps);
+
+console.log(median(ps));
+console.log(arithmeticMean(ps));
+console.log(geometricMean(ps));
+console.log(geometricMeanOfOdds(ps));
+console.log(extremizedGeometricMeanOfOdds(ps, 1.5));
+console.log(extremizedGeometricMeanOfOdds(ps, 2.5));
+console.log(neyman(ps));

package-lock.json

@@ -0,0 +1,28 @@
+{
+  "name": "forecasting",
+  "version": "0.0.1",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "forecasting",
+      "version": "0.0.1",
+      "license": "ISC",
+      "dependencies": {
+        "@forecasting/aggregation": "^0.0.1"
+      }
+    },
+    "node_modules/@forecasting/aggregation": {
+      "version": "0.0.1",
+      "resolved": "https://registry.npmjs.org/@forecasting/aggregation/-/aggregation-0.0.1.tgz",
+      "integrity": "sha512-N6NwJaHioyJQZwjvbNYQCknegrQoWne5I1TILA0jSu+8xkCIN+16cYumc1hZSYAKTzfBsiQWXZbuubfVMpgXFg=="
+    }
+  },
+  "dependencies": {
+    "@forecasting/aggregation": {
+      "version": "0.0.1",
+      "resolved": "https://registry.npmjs.org/@forecasting/aggregation/-/aggregation-0.0.1.tgz",
+      "integrity": "sha512-N6NwJaHioyJQZwjvbNYQCknegrQoWne5I1TILA0jSu+8xkCIN+16cYumc1hZSYAKTzfBsiQWXZbuubfVMpgXFg=="
+    }
+  }
+}

package.json

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+{
+  "name": "forecasting",
+  "version": "0.0.1",
+  "description": "Forecasting related utilities.",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "keywords": [
+    "forecasting",
+    "prediction"
+  ],
+  "author": "Nuño Sempere",
+  "license": "ISC",
+  "dependencies": {
+    "@forecasting/aggregation": "^0.0.1"
+  }
+}