Merged with develop

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,5 @@
+blank_issues_enabled: true
+contact_links:
+  - name: Ideas and feature requests - Squiggle Discussions on GitHub
+    url: https://github.com/quantified-uncertainty/squiggle/discussions
+    about: Please propose and discuss new features here. Remember to search for your idea before posting a new topic! Where would you like to see Squiggle go over the next few months, several months, or few years?

.github/ISSUE_TEMPLATE/future.md

@@ -1,6 +0,0 @@
----
-name: Idea or feature request
-about: Where would you like to see Squiggle go over the next few months, several months, or few years?
----
-
-# Description

.github/ISSUE_TEMPLATE/ops-testing.md

@@ -6,7 +6,9 @@ labels: "ops & testing"
 
 # Description:
 
-<!-- delete this section if testing task or otherwise not applicable --> 
+
 # The OS and version, yarn version, etc. in which this came up
 
+<!-- delete this section if testing task or otherwise not applicable -->
+
 # Desired behavior

.github/workflows/ci.yml

@@ -4,14 +4,10 @@ on:
   push:
     branches:
       - master
-      - production
-      - staging
       - develop
   pull_request:
     branches:
       - master
-      - production
-      - staging
       - develop
 
 jobs:

.github/workflows/codeql-analysis.yml

@@ -62,7 +62,7 @@ jobs:
       # If this step fails, then you should remove it and run the build manually (see below)
       - name: Autobuild
         uses: github/codeql-action/autobuild@v1
-      - name: Install dependencies 
+      - name: Install dependencies
         run: yarn
       - name: Build rescript
         run: cd packages/squiggle-lang && yarn build

packages/components/package.json

@@ -9,7 +9,7 @@
     "@testing-library/user-event": "^14.0.4",
     "@types/jest": "^27.4.0",
     "@types/lodash": "^4.14.181",
-    "@types/node": "^17.0.23",
+    "@types/node": "^17.0.24",
     "@types/react": "^18.0.3",
     "@types/react-dom": "^18.0.0",
     "@types/styled-components": "^5.1.25",
@@ -68,14 +68,14 @@
   },
   "devDependencies": {
     "@babel/plugin-proposal-private-property-in-object": "^7.16.7",
-    "@storybook/addon-actions": "^6.4.20",
-    "@storybook/addon-essentials": "^6.4.20",
-    "@storybook/addon-links": "^6.4.20",
-    "@storybook/builder-webpack5": "^6.4.20",
-    "@storybook/manager-webpack5": "^6.4.20",
-    "@storybook/node-logger": "^6.4.20",
+    "@storybook/addon-actions": "^6.4.22",
+    "@storybook/addon-essentials": "^6.4.22",
+    "@storybook/addon-links": "^6.4.22",
+    "@storybook/builder-webpack5": "^6.4.22",
+    "@storybook/manager-webpack5": "^6.4.22",
+    "@storybook/node-logger": "^6.4.22",
     "@storybook/preset-create-react-app": "^4.1.0",
-    "@storybook/react": "^6.4.20",
+    "@storybook/react": "^6.4.22",
     "@types/styled-components": "^5.1.24",
     "@types/webpack": "^5.28.0",
     "react-codejar": "^1.1.2",

packages/squiggle-lang/__tests__/Distributions/Invariants/Means_test.res

@@ -1,15 +1,64 @@
 /*
 This is the most basic file in our invariants family of tests. 
 
-See document in https://github.com/quantified-uncertainty/squiggle/pull/238 for details
+Validate that the addition of means equals the mean of the addition, similar for subtraction and multiplication. 
 
-Note: digits parameter should be higher than -4. 
+Details in https://develop--squiggle-documentation.netlify.app/docs/internal/invariants/
+
+Note: epsilon of 1e3 means the invariants are, in general, not being satisfied. 
 */
 
 open Jest
 open Expect
 open TestHelpers
 
+module Internals = {
+  let epsilon = 5e1
+
+  let mean = GenericDist_Types.Constructors.UsingDists.mean
+
+  let expectImpossiblePath: string => assertion = algebraicOp =>
+    `${algebraicOp} has`->expect->toEqual("failed")
+
+  let distributions = list{
+    normalMake(4e0, 1e0),
+    betaMake(2e0, 4e0),
+    exponentialMake(1.234e0),
+    uniformMake(7e0, 1e1),
+    // cauchyMake(1e0, 1e0),
+    lognormalMake(2e0, 1e0),
+    triangularMake(1e0, 1e1, 5e1),
+    Ok(floatMake(1e1)),
+  }
+  let pairsOfDifferentDistributions = E.L.combinations2(distributions)
+
+  let runMean: DistributionTypes.genericDist => float = dist => {
+    dist->mean->run->toFloat->E.O2.toExn("Shouldn't see this because we trust testcase input")
+  }
+
+  let testOperationMean = (
+    distOp: (
+      DistributionTypes.genericDist,
+      DistributionTypes.genericDist,
+    ) => result<DistributionTypes.genericDist, DistributionTypes.error>,
+    description: string,
+    floatOp: (float, float) => float,
+    dist1': SymbolicDistTypes.symbolicDist,
+    dist2': SymbolicDistTypes.symbolicDist,
+    ~epsilon: float,
+  ) => {
+    let dist1 = dist1'->DistributionTypes.Symbolic
+    let dist2 = dist2'->DistributionTypes.Symbolic
+    let received =
+      distOp(dist1, dist2)->E.R2.fmap(mean)->E.R2.fmap(run)->E.R2.fmap(toFloat)->E.R.toExn
+    let expected = floatOp(runMean(dist1), runMean(dist2))
+    switch received {
+    | None => expectImpossiblePath(description)
+    | Some(x) => expectErrorToBeBounded(x, expected, ~epsilon)
+    }
+  }
+}
+
 let {
   algebraicAdd,
   algebraicMultiply,
@@ -26,115 +75,82 @@ let algebraicSubtract = algebraicSubtract(~env)
 let algebraicLogarithm = algebraicLogarithm(~env)
 let algebraicPower = algebraicPower(~env)
 
-describe("Mean", () => {
-  let digits = -4
+let {testOperationMean, distributions, pairsOfDifferentDistributions, epsilon} = module(Internals)
 
-  let mean = GenericDist_Types.Constructors.UsingDists.mean
+describe("Means are invariant", () => {
+  describe("for addition", () => {
+    let testAdditionMean = testOperationMean(algebraicAdd, "algebraicAdd", \"+.", ~epsilon)
 
-  let runMean: result<DistributionTypes.genericDist, DistributionTypes.error> => float = distR => {
-    distR
-    ->E.R2.fmap(mean)
-    ->E.R2.fmap(run)
-    ->E.R2.fmap(toFloat)
-    ->E.R.toExn
-    ->E.O2.toExn("Shouldn't see this because we trust testcase input")
-  }
-
-  let impossiblePath: string => assertion = algebraicOp =>
-    `${algebraicOp} has`->expect->toEqual("failed")
-
-  let distributions = list{
-    normalMake(0.0, 1e0),
-    betaMake(2e0, 4e0),
-    exponentialMake(1.234e0),
-    uniformMake(7e0, 1e1),
-    // cauchyMake(1e0, 1e0),
-    lognormalMake(1e0, 1e0),
-    triangularMake(1e0, 1e1, 5e1),
-    Ok(floatMake(1e1)),
-  }
-  let combinations = E.L.combinations2(distributions)
-  let zipDistsDists = E.L.zip(distributions, distributions)
-
-  let testOperationMean = (
-      distOp: (DistributionTypes.genericDist, DistributionTypes.genericDist) => result<DistributionTypes.genericDist, DistributionTypes.error>, 
-      description: string, 
-      floatOp: (float, float) => float, 
-      dist1': result<SymbolicDistTypes.symbolicDist, string>, 
-      dist2': result<SymbolicDistTypes.symbolicDist, string>
-    ) => {
-    let dist1 = dist1'->E.R2.fmap(x=>DistributionTypes.Symbolic(x))->E.R2.fmap2(s=>DistributionTypes.Other(s))
-    let dist2 = dist2'->E.R2.fmap(x=>DistributionTypes.Symbolic(x))->E.R2.fmap2(s=>DistributionTypes.Other(s))
-    let received =
-      E.R.liftJoin2(distOp, dist1, dist2)
-      ->E.R2.fmap(mean)
-      ->E.R2.fmap(run)
-      ->E.R2.fmap(toFloat)
-    let expected = floatOp(runMean(dist1), runMean(dist2))
-    switch received {
-    | Error(err) => impossiblePath(description)
-    | Ok(x) =>
-      switch x {
-      | None => impossiblePath(description)
-      | Some(x) => x->expect->toBeSoCloseTo(expected, ~digits)
-      }
-    }
-  }
-
-  describe("addition", () => {
-    let testAdditionMean = testOperationMean(algebraicAdd, "algebraicAdd", \"+.")  
-
-    testAll("homogeneous addition", zipDistsDists, dists => {
-      let (dist1, dist2) = dists
-      testAdditionMean(dist1, dist2)
+    testAll("with two of the same distribution", distributions, dist => {
+      E.R.liftM2(testAdditionMean, dist, dist)->E.R.toExn
     })
 
-    testAll("heterogeneous addition (1)", combinations, dists => {
+    testAll("with two different distributions", pairsOfDifferentDistributions, dists => {
       let (dist1, dist2) = dists
-      testAdditionMean(dist1, dist2)
+      E.R.liftM2(testAdditionMean, dist1, dist2)->E.R.toExn
     })
 
-    testAll("heterogeneous addition (commuted of 1 (or; 2))", combinations, dists => {
-      let (dist1, dist2) = dists
-      testAdditionMean(dist2, dist1)
-    })
+    testAll(
+      "with two different distributions in swapped order",
+      pairsOfDifferentDistributions,
+      dists => {
+        let (dist1, dist2) = dists
+        E.R.liftM2(testAdditionMean, dist2, dist1)->E.R.toExn
+      },
+    )
   })
 
-  describe("subtraction", () => {
-    let testSubtractionMean = testOperationMean(algebraicSubtract, "algebraicSubtract", \"-.")
+  describe("for subtraction", () => {
+    let testSubtractionMean = testOperationMean(
+      algebraicSubtract,
+      "algebraicSubtract",
+      \"-.",
+      ~epsilon,
+    )
 
-    testAll("homogeneous subtraction", zipDistsDists, dists => {
-      let (dist1, dist2) = dists
-      testSubtractionMean(dist1, dist2)
+    testAll("with two of the same distribution", distributions, dist => {
+      E.R.liftM2(testSubtractionMean, dist, dist)->E.R.toExn
     })
 
-    testAll("heterogeneous subtraction (1)", combinations, dists => {
+    testAll("with two different distributions", pairsOfDifferentDistributions, dists => {
       let (dist1, dist2) = dists
-      testSubtractionMean(dist1, dist2)
+      E.R.liftM2(testSubtractionMean, dist1, dist2)->E.R.toExn
     })
 
-    testAll("heterogeneous subtraction (commuted of 1 (or; 2))", combinations, dists => {
-      let (dist1, dist2) = dists
-      testSubtractionMean(dist2, dist1)
-    })
+    testAll(
+      "with two different distributions in swapped order",
+      pairsOfDifferentDistributions,
+      dists => {
+        let (dist1, dist2) = dists
+        E.R.liftM2(testSubtractionMean, dist2, dist1)->E.R.toExn
+      },
+    )
   })
 
-  describe("multiplication", () => {
-    let testMultiplicationMean = testOperationMean(algebraicMultiply, "algebraicMultiply", \"*.")
+  describe("for multiplication", () => {
+    let testMultiplicationMean = testOperationMean(
+      algebraicMultiply,
+      "algebraicMultiply",
+      \"*.",
+      ~epsilon,
+    )
 
-    testAll("homogeneous subtraction", zipDistsDists, dists => {
-      let (dist1, dist2) = dists
-      testMultiplicationMean(dist1, dist2)
+    testAll("with two of the same distribution", distributions, dist => {
+      E.R.liftM2(testMultiplicationMean, dist, dist)->E.R.toExn
     })
 
-    testAll("heterogeneoous subtraction (1)", combinations, dists => {
+    testAll("with two different distributions", pairsOfDifferentDistributions, dists => {
       let (dist1, dist2) = dists
-      testMultiplicationMean(dist1, dist2)
+      E.R.liftM2(testMultiplicationMean, dist1, dist2)->E.R.toExn
     })
 
-    testAll("heterogeneoous subtraction (commuted of 1 (or; 2))", combinations, dists => {
-      let (dist1, dist2) = dists
-      testMultiplicationMean(dist2, dist1)
-    })
+    testAll(
+      "with two different distributions in swapped order",
+      pairsOfDifferentDistributions,
+      dists => {
+        let (dist1, dist2) = dists
+        E.R.liftM2(testMultiplicationMean, dist2, dist1)->E.R.toExn
+      },
+    )
   })
 })

packages/squiggle-lang/__tests__/ReducerInterface/ReducerInterface_Distribution_test.res

@@ -19,12 +19,12 @@ describe("eval on distribution functions", () => {
     testEval("lognormal(5,2)", "Ok(Lognormal(5,2))")
   })
   describe("unaryMinus", () => {
-    testEval("mean(-normal(5,2))", "Ok(-5.002887370380851)")
+    testEval("mean(-normal(5,2))", "Ok(-5)")
   })
   describe("to", () => {
     testEval("5 to 2", "Error(TODO: Low value must be less than high value.)")
-    testEval("to(2,5)", "Ok(Lognormal(1.1512925464970227,0.278507821238345))")
-    testEval("to(-2,2)", "Ok(Normal(0,1.215913388057542))")
+    testEval("to(2,5)", "Ok(Lognormal(1.1512925464970227,0.27853260523016377))")
+    testEval("to(-2,2)", "Ok(Normal(0,1.2159136638235384))")
   })
   describe("mean", () => {
     testEval("mean(normal(5,2))", "Ok(5)")
@@ -45,10 +45,30 @@ describe("eval on distribution functions", () => {
   describe("add", () => {
     testEval("add(normal(5,2), normal(10,2))", "Ok(Normal(15,2.8284271247461903))")
     testEval("add(normal(5,2), lognormal(10,2))", "Ok(Sample Set Distribution)")
-    testEval("add(normal(5,2), 3)", "Ok(Point Set Distribution)")
-    testEval("add(3, normal(5,2))", "Ok(Point Set Distribution)")
-    testEval("3+normal(5,2)", "Ok(Point Set Distribution)")
-    testEval("normal(5,2)+3", "Ok(Point Set Distribution)")
+    testEval("add(normal(5,2), 3)", "Ok(Normal(8,2))")
+    testEval("add(3, normal(5,2))", "Ok(Normal(8,2))")
+    testEval("3+normal(5,2)", "Ok(Normal(8,2))")
+    testEval("normal(5,2)+3", "Ok(Normal(8,2))")
+  })
+  describe("subtract", () => {
+    testEval("10 - normal(5, 1)", "Ok(Normal(5,1))")
+    testEval("normal(5, 1) - 10", "Ok(Normal(-5,1))")
+  })
+  describe("multiply", () => {
+    testEval("normal(10, 2) * 2", "Ok(Normal(20,4))")
+    testEval("2 * normal(10, 2)", "Ok(Normal(20,4))")
+    testEval("lognormal(5,2) * lognormal(10,2)", "Ok(Lognormal(15,2.8284271247461903))")
+    testEval("lognormal(10, 2) * lognormal(5, 2)", "Ok(Lognormal(15,2.8284271247461903))")
+    testEval("2 * lognormal(5, 2)", "Ok(Lognormal(5.693147180559945,2))")
+    testEval("lognormal(5, 2) * 2", "Ok(Lognormal(5.693147180559945,2))")
+  })
+  describe("division", () => {
+    testEval("lognormal(5,2) / lognormal(10,2)", "Ok(Lognormal(-5,4))")
+    testEval("lognormal(10,2) / lognormal(5,2)", "Ok(Lognormal(5,4))")
+    testEval("lognormal(5, 2) / 2", "Ok(Lognormal(4.306852819440055,2))")
+    testEval("2 / lognormal(5, 2)", "Ok(Lognormal(-4.306852819440055,2))")
+    testEval("2 / normal(10, 2)", "Ok(Point Set Distribution)")
+    testEval("normal(10, 2) / 2", "Ok(Normal(5,1))")
   })
   describe("truncate", () => {
     testEval("truncateLeft(normal(5,2), 3)", "Ok(Point Set Distribution)")
@@ -101,6 +121,10 @@ describe("parse on distribution functions", () => {
     testParse("3 ^ normal(5,1)", "Ok((:pow 3 (:normal 5 1)))")
     testParse("normal(5,2) ^ 3", "Ok((:pow (:normal 5 2) 3))")
   })
+  describe("subtraction", () => {
+    testParse("10 - normal(5,1)", "Ok((:subtract 10 (:normal 5 1)))")
+    testParse("normal(5,1) - 10", "Ok((:subtract (:normal 5 1) 10))")
+  })
   describe("pointwise arithmetic expressions", () => {
     testParse(~skip=true, "normal(5,2) .+ normal(5,1)", "Ok((:dotAdd (:normal 5 2) (:normal 5 1)))")
     testParse(

packages/squiggle-lang/__tests__/TestHelpers.res

@@ -1,6 +1,25 @@
 open Jest
 open Expect
 
+/*
+This encodes the expression for percent error
+The test says "the percent error of received against expected is bounded by epsilon"
+
+However, the semantics are degraded by catching some numerical instability: 
+when expected is too small, the return of this function might blow up to infinity. 
+So we capture that by taking the max of abs(expected) against a 1. 
+
+A sanity check of this function would be welcome, in general it is a better way of approaching 
+squiggle-lang tests than toBeSoCloseTo. 
+*/
+let expectErrorToBeBounded = (received, expected, ~epsilon) => {
+  let distance = Js.Math.abs_float(received -. expected)
+  let expectedAbs = Js.Math.abs_float(expected)
+  let normalizingDenom = Js.Math.max_float(expectedAbs, 1e0)
+  let error = distance /. normalizingDenom
+  error->expect->toBeLessThan(epsilon)
+}
+
 let makeTest = (~only=false, str, item1, item2) =>
   only
     ? Only.test(str, () => expect(item1)->toEqual(item2))

packages/squiggle-lang/src/rescript/Distributions/SymbolicDist/SymbolicDist.res

@@ -1,5 +1,8 @@
 open SymbolicDistTypes
 
+let normal95confidencePoint = 1.6448536269514722
+// explained in website/docs/internal/ProcessingConfidenceIntervals
+
 module Normal = {
   type t = normal
   let make = (mean: float, stdev: float): result<symbolicDist, string> =>
@@ -11,7 +14,7 @@ module Normal = {
 
   let from90PercentCI = (low, high) => {
     let mean = E.A.Floats.mean([low, high])
-    let stdev = (high -. low) /. (2. *. 1.644854)
+    let stdev = (high -. low) /. (2. *. normal95confidencePoint)
     #Normal({mean: mean, stdev: stdev})
   }
   let inv = (p, t: t) => Jstat.Normal.inv(p, t.mean, t.stdev)
@@ -21,12 +24,12 @@ module Normal = {
 
   let add = (n1: t, n2: t) => {
     let mean = n1.mean +. n2.mean
-    let stdev = sqrt(n1.stdev ** 2. +. n2.stdev ** 2.)
+    let stdev = Js.Math.sqrt(n1.stdev ** 2. +. n2.stdev ** 2.)
     #Normal({mean: mean, stdev: stdev})
   }
   let subtract = (n1: t, n2: t) => {
     let mean = n1.mean -. n2.mean
-    let stdev = sqrt(n1.stdev ** 2. +. n2.stdev ** 2.)
+    let stdev = Js.Math.sqrt(n1.stdev ** 2. +. n2.stdev ** 2.)
     #Normal({mean: mean, stdev: stdev})
   }
 
@@ -44,6 +47,23 @@ module Normal = {
     | #Subtract => Some(subtract(n1, n2))
     | _ => None
     }
+
+  let operateFloatFirst = (operation: Operation.Algebraic.t, n1: float, n2: t) =>
+    switch operation {
+    | #Add => Some(#Normal({mean: n1 +. n2.mean, stdev: n2.stdev}))
+    | #Subtract => Some(#Normal({mean: n1 -. n2.mean, stdev: n2.stdev}))
+    | #Multiply => Some(#Normal({mean: n1 *. n2.mean, stdev: n1 *. n2.stdev}))
+    | _ => None
+    }
+
+  let operateFloatSecond = (operation: Operation.Algebraic.t, n1: t, n2: float) =>
+    switch operation {
+    | #Add => Some(#Normal({mean: n1.mean +. n2, stdev: n1.stdev}))
+    | #Subtract => Some(#Normal({mean: n1.mean -. n2, stdev: n1.stdev}))
+    | #Multiply => Some(#Normal({mean: n1.mean *. n2, stdev: n1.stdev *. n2}))
+    | #Divide => Some(#Normal({mean: n1.mean /. n2, stdev: n1.stdev /. n2}))
+    | _ => None
+    }
 }
 
 module Exponential = {
@@ -115,19 +135,22 @@ module Lognormal = {
   let mean = (t: t) => Ok(Jstat.Lognormal.mean(t.mu, t.sigma))
   let sample = (t: t) => Jstat.Lognormal.sample(t.mu, t.sigma)
   let toString = ({mu, sigma}: t) => j`Lognormal($mu,$sigma)`
+
   let from90PercentCI = (low, high) => {
     let logLow = Js.Math.log(low)
     let logHigh = Js.Math.log(high)
     let mu = E.A.Floats.mean([logLow, logHigh])
-    let sigma = (logHigh -. logLow) /. (2.0 *. 1.645)
+    let sigma = (logHigh -. logLow) /. (2.0 *. normal95confidencePoint)
     #Lognormal({mu: mu, sigma: sigma})
   }
   let fromMeanAndStdev = (mean, stdev) => {
+    // https://math.stackexchange.com/questions/2501783/parameters-of-a-lognormal-distribution
+    // https://wikiless.org/wiki/Log-normal_distribution?lang=en#Generation_and_parameters
     if stdev > 0.0 {
-      let variance = Js.Math.pow_float(~base=stdev, ~exp=2.0)
-      let meanSquared = Js.Math.pow_float(~base=mean, ~exp=2.0)
-      let mu = Js.Math.log(mean) -. 0.5 *. Js.Math.log(variance /. meanSquared +. 1.0)
-      let sigma = Js.Math.pow_float(~base=Js.Math.log(variance /. meanSquared +. 1.0), ~exp=0.5)
+      let variance = stdev ** 2.
+      let meanSquared = mean ** 2.
+      let mu = 2. *. Js.Math.log(mean) -. 0.5 *. Js.Math.log(variance +. meanSquared)
+      let sigma = Js.Math.sqrt(Js.Math.log(variance /. meanSquared +. 1.))
       Ok(#Lognormal({mu: mu, sigma: sigma}))
     } else {
       Error("Lognormal standard deviation must be larger than 0")
@@ -135,8 +158,9 @@ module Lognormal = {
   }
 
   let multiply = (l1, l2) => {
+    // https://wikiless.org/wiki/Log-normal_distribution?lang=en#Multiplication_and_division_of_independent,_log-normal_random_variables
     let mu = l1.mu +. l2.mu
-    let sigma = l1.sigma +. l2.sigma
+    let sigma = Js.Math.sqrt(l1.sigma ** 2. +. l2.sigma ** 2.) // m
     #Lognormal({mu: mu, sigma: sigma})
   }
   let divide = (l1, l2) => {
@@ -152,6 +176,22 @@ module Lognormal = {
     | #Divide => Some(divide(n1, n2))
     | _ => None
     }
+
+  let operateFloatFirst = (operation: Operation.Algebraic.t, n1: float, n2: t) =>
+    switch operation {
+    | #Multiply =>
+      n1 > 0.0 ? Some(#Lognormal({mu: Js.Math.log(n1) +. n2.mu, sigma: n2.sigma})) : None
+    | #Divide => n1 > 0.0 ? Some(#Lognormal({mu: Js.Math.log(n1) -. n2.mu, sigma: n2.sigma})) : None
+    | _ => None
+    }
+
+  let operateFloatSecond = (operation: Operation.Algebraic.t, n1: t, n2: float) =>
+    switch operation {
+    | #Multiply =>
+      n2 > 0.0 ? Some(#Lognormal({mu: n1.mu +. Js.Math.log(n2), sigma: n1.sigma})) : None
+    | #Divide => n2 > 0.0 ? Some(#Lognormal({mu: n1.mu -. Js.Math.log(n2), sigma: n1.sigma})) : None
+    | _ => None
+    }
 }
 
 module Uniform = {
@@ -343,8 +383,28 @@ module T = {
       }
     | (#Normal(v1), #Normal(v2)) =>
       Normal.operate(op, v1, v2) |> E.O.dimap(r => #AnalyticalSolution(r), () => #NoSolution)
+    | (#Float(v1), #Normal(v2)) =>
+      Normal.operateFloatFirst(op, v1, v2) |> E.O.dimap(
+        r => #AnalyticalSolution(r),
+        () => #NoSolution,
+      )
+    | (#Normal(v1), #Float(v2)) =>
+      Normal.operateFloatSecond(op, v1, v2) |> E.O.dimap(
+        r => #AnalyticalSolution(r),
+        () => #NoSolution,
+      )
     | (#Lognormal(v1), #Lognormal(v2)) =>
       Lognormal.operate(op, v1, v2) |> E.O.dimap(r => #AnalyticalSolution(r), () => #NoSolution)
+    | (#Float(v1), #Lognormal(v2)) =>
+      Lognormal.operateFloatFirst(op, v1, v2) |> E.O.dimap(
+        r => #AnalyticalSolution(r),
+        () => #NoSolution,
+      )
+    | (#Lognormal(v1), #Float(v2)) =>
+      Lognormal.operateFloatSecond(op, v1, v2) |> E.O.dimap(
+        r => #AnalyticalSolution(r),
+        () => #NoSolution,
+      )
     | _ => #NoSolution
     }
 

packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res

@@ -117,7 +117,8 @@ module Helpers = {
         | Error(err) => GenDistError(ArgumentError(err))
         }
       }
-    | Some(EvDistribution(b)) => switch parseDistributionArray(args) {
+    | Some(EvDistribution(b)) =>
+      switch parseDistributionArray(args) {
       | Ok(distributions) => mixtureWithDefaultWeights(distributions)
       | Error(err) => GenDistError(ArgumentError(err))
       }

packages/squiggle-lang/src/rescript/Utility/E.res

@@ -215,8 +215,8 @@ module R2 = {
     | Ok(r) => Ok(r)
     | Error(e) => map(e)
     }
-  
-  let fmap2 = (xR, f) => 
+
+  let fmap2 = (xR, f) =>
     switch xR {
     | Ok(x) => x->Ok
     | Error(x) => x->f->Error

packages/website/README.md

@@ -2,21 +2,24 @@
 
 This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
 
-## Build for development and production
+# Build for development
 
-This one actually works without running `yarn` at the monorepo level, but it doesn't hurt. You must at least run it at this package level
+We assume you ran `yarn` at monorepo level.
+
+The website depends on `squiggle-lang`, which you have to build manually.
 
 ```sh
-yarn
+cd ../squiggle-lang
+yarn build
 ```
 
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
+Generate static content, to the `build` directory.
 
 ```sh
 yarn build
 ```
 
-Your local dev server is here, opening up a browser window.
+Open a local dev server
 
 ```sh
 yarn start

packages/website/docs/Internal/Invariants.md

@@ -0,0 +1,126 @@
+---
+title: Statistical properties of algebraic combinations of distributions for property testing.
+urlcolor: blue
+author:
+  - Nuño Sempere
+  - Quinn Dougherty
+abstract: This document outlines some properties about algebraic combinations of distributions. It is meant to facilitate property tests for [Squiggle](https://squiggle-language.com/), an estimation language for forecasters. So far, we are focusing on the means, the standard deviation and the shape of the pdfs.
+---
+
+_This document right now is normative and aspirational, not a description of the testing that's currently done_.
+
+The academic keyword to search for in relation to this document is "[algebra of random variables](https://wikiless.org/wiki/Algebra_of_random_variables?lang=en)". Squiggle doesn't yet support getting the standard deviation, denoted by $\sigma$, but such support could yet be added.
+
+## Means and standard deviations
+
+### Sums
+
+$$
+mean(f+g) = mean(f) + mean(g)
+$$
+
+$$
+\sigma(f+g) = \sqrt{\sigma(f)^2 + \sigma(g)^2}
+$$
+
+In the case of normal distributions,
+
+$$
+mean(normal(a,b) + normal(c,d)) = mean(normal(a+c, \sqrt{b^2 + d^2}))
+$$
+
+### Subtractions
+
+$$
+mean(f-g) = mean(f) - mean(g)
+$$
+
+$$
+\sigma(f-g) = \sqrt{\sigma(f)^2 + \sigma(g)^2}
+$$
+
+### Multiplications
+
+$$
+mean(f \cdot g) = mean(f) \cdot mean(g)
+$$
+
+$$
+\sigma(f \cdot g) = \sqrt{ (\sigma(f)^2 + mean(f)) \cdot (\sigma(g)^2 + mean(g)) - (mean(f) \cdot mean(g))^2}
+$$
+
+### Divisions
+
+Divisions are tricky, and in general we don't have good expressions to characterize properties of ratios. In particular, the ratio of two normals is a Cauchy distribution, which doesn't have to have a mean.
+
+## Probability density functions (pdfs)
+
+Specifying the pdf of the sum/multiplication/... of distributions as a function of the pdfs of the individual arguments can still be done. But it requires integration. My sense is that this is still doable, and I (Nuño) provide some _pseudocode_ to do this.
+
+### Sums
+
+Let $f, g$ be two independently distributed functions. Then, the pdf of their sum, evaluated at a point $z$, expressed as $(f + g)(z)$, is given by:
+
+$$
+(f + g)(z)= \int_{-\infty}^{\infty} f(x)\cdot g(z-x) \,dx
+$$
+
+See a proof sketch [here](https://www.milefoot.com/math/stat/rv-sums.htm)
+
+Here is some pseudocode to approximate this:
+
+```js
+// pdf1 and pdf2 are pdfs,
+// and cdf1 and cdf2 are their corresponding cdfs
+
+let epsilonForBounds = 2 ** -16;
+let getBounds = (cdf) => {
+  let cdf_min = -1;
+  let cdf_max = 1;
+  let n = 0;
+  while (
+    (cdf(cdf_min) > epsilonForBounds || 1 - cdf(cdf_max) > epsilonForBounds) &&
+    n < 10
+  ) {
+    if (cdf(cdf_min) > epsilonForBounds) {
+      cdf_min = cdf_min * 2;
+    }
+    if (1 - cdf(cdf_max) > epsilonForBounds) {
+      cdf_max = cdf_max * 2;
+    }
+  }
+  return [cdf_min, cdf_max];
+};
+
+let epsilonForIntegrals = 2 ** -16;
+let pdfOfSum = (pdf1, pdf2, cdf1, cdf2, z) => {
+  let bounds1 = getBounds(cdf1);
+  let bounds2 = getBounds(cdf2);
+  let bounds = [
+    Math.min(bounds1[0], bounds2[0]),
+    Math.max(bounds1[1], bounds2[1]),
+  ];
+
+  let result = 0;
+  for (let x = bounds[0]; (x = x + epsilonForIntegrals); x < bounds[1]) {
+    let delta = pdf1(x) * pdf2(z - x);
+    result = result + delta * epsilonForIntegrals;
+  }
+  return result;
+};
+```
+
+## Cumulative density functions
+
+TODO
+
+## Inverse cumulative density functions
+
+TODO
+
+# To do:
+
+- Provide sources or derivations, useful as this document becomes more complicated
+- Provide definitions for the probability density function, exponential, inverse, log, etc.
+- Provide at least some tests for division
+- See if playing around with characteristic functions turns out anything useful

packages/website/docs/Internal/ProcessingConfidenceIntervals.md

@@ -0,0 +1,32 @@
+# Processing confidence intervals
+
+This page explains what we are doing when we take a 95% confidence interval, and we get a mean and a standard deviation from it
+
+## For normals
+
+```js
+module Normal = {
+  //...
+  let from90PercentCI = (low, high) => {
+    let mean = E.A.Floats.mean([low, high])
+    let stdev = (high -. low) /. (2. *. 1.6448536269514722)
+    #Normal({mean: mean, stdev: stdev})
+  }
+  //...
+}
+```
+
+We know that for a normal with mean $\mu$ and standard deviation $\sigma$,
+
+$$
+
+a \cdot Normal(\mu, \sigma) = Normal(a\cdot \mu, |a|\cdot \sigma)
+
+
+$$
+
+We can now look at the inverse cdf of a $Normal(0,1)$. We find that the 95% point is reached at $1.6448536269514722$. ([source](https://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p)) This means that the 90% confidence interval is $[-1.6448536269514722, 1.6448536269514722]$, which has a width of $2 \cdot 1.6448536269514722$.
+
+So then, if we take a $Normal(0,1)$ and we multiply it by $\frac{(high -. low)}{(2. *. 1.6448536269514722)}$, it's 90% confidence interval will be multiplied by the same amount. Then we just have to shift it by the mean to get our target normal.
+
+## For lognormals

packages/website/docusaurus.config.js

@@ -1,5 +1,7 @@
 // @ts-check
 // Note: type annotations allow type checking and IDEs autocompletion
+const math = require("remark-math");
+const katex = require("rehype-katex");
 
 const lightCodeTheme = require("prism-react-renderer/themes/github");
 const darkCodeTheme = require("prism-react-renderer/themes/dracula");
@@ -14,7 +16,7 @@ const config = {
   onBrokenLinks: "throw",
   onBrokenMarkdownLinks: "warn",
   favicon: "img/favicon.ico",
-  organizationName: "QURIResearch", // Usually your GitHub org/user name.
+  organizationName: "quantified-uncertainty", // Usually your GitHub org/user name.
   projectName: "squiggle", // Usually your repo name.
 
   plugins: [
@@ -47,13 +49,15 @@ const config = {
           sidebarPath: require.resolve("./sidebars.js"),
           // Please change this to your repo.
           editUrl:
-            "https://github.com/foretold-app/squiggle/tree/master/packages/website/",
+            "https://github.com/quantified-uncertainty/squiggle/tree/master/packages/website/",
+          remarkPlugins: [math],
+          rehypePlugins: [katex],
         },
         blog: {
           showReadingTime: true,
           // Please change this to your repo.
           editUrl:
-            "https://github.com/foretold-app/squiggle/tree/master/packages/website/",
+            "https://github.com/quantified-uncertainty/squiggle/tree/master/packages/website/",
         },
         theme: {
           customCss: require.resolve("./src/css/custom.css"),
@@ -111,6 +115,15 @@ const config = {
         darkTheme: darkCodeTheme,
       },
     }),
+  stylesheets: [
+    {
+      href: "https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css",
+      type: "text/css",
+      integrity:
+        "sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM",
+      crossorigin: "anonymous",
+    },
+  ],
 };
 
 module.exports = config;

packages/website/package.json

@@ -17,7 +17,10 @@
     "clsx": "^1.1.1",
     "prism-react-renderer": "^1.2.1",
     "react": "^18.0.0",
-    "react-dom": "^18.0.0"
+    "react-dom": "^18.0.0",
+    "remark-math": "^3",
+    "rehype-katex": "^5",
+    "hast-util-is-element": "2.1.2"
   },
   "browserslist": {
     "production": [

packages/website/sidebars.js

@@ -40,6 +40,16 @@ const sidebars = {
         },
       ],
     },
+    {
+      type: "category",
+      label: "Internal",
+      items: [
+        {
+          type: "autogenerated",
+          dirName: "Internal",
+        },
+      ],
+    },
   ],
 
   // But you can create a sidebar manually