Merge branch 'develop' into multiple-charts

packages/components/src/components/SquigglePlayground.tsx

@@ -215,31 +215,31 @@ const ViewSettings: React.FC<{ register: UseFormRegister<FormFields> }> = ({
             name="minX"
             type="number"
             register={register}
-            label="The minimum of the charted distribution domain"
+            label="Min X Value"
           />
           <InputItem
             name="maxX"
             type="number"
             register={register}
-            label="The maximum of the charted distribution domain"
+            label="Max X Value"
           />
           <InputItem
             name="title"
             type="text"
             register={register}
-            label="The title shown on the distribution"
+            label="Title"
           />
           <InputItem
             name="tickFormat"
             type="text"
             register={register}
-            label="The format that the ticks are rendered"
+            label="Tick Format"
           />
           <InputItem
             name="color"
             type="color"
             register={register}
-            label="The color of the charted distribution"
+            label="Color"
           />
         </div>
       </HeadedSection>
@@ -521,7 +521,7 @@ export const SquigglePlayground: FC<PlaygroundProps> = ({
   );
 
   const withEditor = (
-    <div className="flex mt-1">
+    <div className="flex mt-2">
       <div className="w-1/2">{tabs}</div>
       <div className="w-1/2 p-2 pl-4">{squiggleChart}</div>
     </div>

packages/components/src/components/ui/Toggle.tsx

@@ -23,8 +23,8 @@ export const Toggle: React.FC<Props> = ({
       layout
       transition={{ duration: 0.2 }}
       className={clsx(
-        "rounded-full py-1 bg-indigo-500 text-white text-xs font-semibold flex items-center space-x-1",
+        "rounded-md py-0.5 bg-slate-500 text-white text-xs font-semibold flex items-center space-x-1",
-        status ? "bg-indigo-500" : "bg-gray-400",
+        status ? "bg-slate-500" : "bg-gray-400",
         status ? "pl-1 pr-3" : "pl-3 pr-1",
         !status && "flex-row-reverse space-x-reverse"
       )}

packages/squiggle-lang/__tests__/Distributions/DistributionOperation_test.res

@@ -1,7 +1,7 @@
 open Jest
 open Expect
 
-let env: DistributionOperation.env = {
+let env: GenericDist.env = {
   sampleCount: 100,
   xyPointLength: 100,
 }
@@ -34,7 +34,7 @@ describe("sparkline", () => {
     expected: DistributionOperation.outputType,
   ) => {
     test(name, () => {
-      let result = DistributionOperation.run(~env, FromDist(ToString(ToSparkline(20)), dist))
+      let result = DistributionOperation.run(~env, FromDist(#ToString(ToSparkline(20)), dist))
       expect(result)->toEqual(expected)
     })
   }
@@ -81,8 +81,8 @@ describe("sparkline", () => {
 describe("toPointSet", () => {
   test("on symbolic normal distribution", () => {
     let result =
-      run(FromDist(ToDist(ToPointSet), normalDist5))
+      run(FromDist(#ToDist(ToPointSet), normalDist5))
-      ->outputMap(FromDist(ToFloat(#Mean)))
+      ->outputMap(FromDist(#ToFloat(#Mean)))
       ->toFloat
       ->toExt
     expect(result)->toBeSoCloseTo(5.0, ~digits=0)
@@ -90,10 +90,10 @@ describe("toPointSet", () => {
 
   test("on sample set", () => {
     let result =
-      run(FromDist(ToDist(ToPointSet), normalDist5))
+      run(FromDist(#ToDist(ToPointSet), normalDist5))
-      ->outputMap(FromDist(ToDist(ToSampleSet(1000))))
+      ->outputMap(FromDist(#ToDist(ToSampleSet(1000))))
-      ->outputMap(FromDist(ToDist(ToPointSet)))
+      ->outputMap(FromDist(#ToDist(ToPointSet)))
-      ->outputMap(FromDist(ToFloat(#Mean)))
+      ->outputMap(FromDist(#ToFloat(#Mean)))
       ->toFloat
       ->toExt
     expect(result)->toBeSoCloseTo(5.0, ~digits=-1)

packages/squiggle-lang/__tests__/Distributions/GenericDist_Fixtures.res

@@ -19,7 +19,6 @@ exception MixtureFailed
 let float1 = 1.0
 let float2 = 2.0
 let float3 = 3.0
-let {mkDelta} = module(TestHelpers)
+let point1 = TestHelpers.mkDelta(float1)
-let point1 = mkDelta(float1)
+let point2 = TestHelpers.mkDelta(float2)
-let point2 = mkDelta(float2)
+let point3 = TestHelpers.mkDelta(float3)
-let point3 = mkDelta(float3)

packages/squiggle-lang/__tests__/Distributions/Mixture_test.res

@@ -11,7 +11,7 @@ describe("mixture", () => {
       let (mean1, mean2) = tup
       let meanValue = {
         run(Mixture([(mkNormal(mean1, 9e-1), 0.5), (mkNormal(mean2, 9e-1), 0.5)]))->outputMap(
-          FromDist(ToFloat(#Mean)),
+          FromDist(#ToFloat(#Mean)),
         )
       }
       meanValue->unpackFloat->expect->toBeSoCloseTo((mean1 +. mean2) /. 2.0, ~digits=-1)
@@ -28,7 +28,7 @@ describe("mixture", () => {
       let meanValue = {
         run(
           Mixture([(mkBeta(alpha, beta), betaWeight), (mkExponential(rate), exponentialWeight)]),
-        )->outputMap(FromDist(ToFloat(#Mean)))
+        )->outputMap(FromDist(#ToFloat(#Mean)))
       }
       let betaMean = 1.0 /. (1.0 +. beta /. alpha)
       let exponentialMean = 1.0 /. rate
@@ -52,7 +52,7 @@ describe("mixture", () => {
             (mkUniform(low, high), uniformWeight),
             (mkLognormal(mu, sigma), lognormalWeight),
           ]),
-        )->outputMap(FromDist(ToFloat(#Mean)))
+        )->outputMap(FromDist(#ToFloat(#Mean)))
       }
       let uniformMean = (low +. high) /. 2.0
       let lognormalMean = mu +. sigma ** 2.0 /. 2.0

packages/squiggle-lang/__tests__/Distributions/Scoring/KlDivergence_test.res

@@ -3,6 +3,7 @@ open Expect
 open TestHelpers
 open GenericDist_Fixtures
 
+let klDivergence = DistributionOperation.Constructors.LogScore.distEstimateDistAnswer(~env)
 // integral from low to high of 1 / (high - low) log(normal(mean, stdev)(x) / (1 / (high - low))) dx
 let klNormalUniform = (mean, stdev, low, high): float =>
   -.Js.Math.log((high -. low) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. stdev ** 2.0)) +.
@@ -11,8 +12,6 @@ let klNormalUniform = (mean, stdev, low, high): float =>
   (mean ** 2.0 -. (high +. low) *. mean +. (low ** 2.0 +. high *. low +. high ** 2.0) /. 3.0)
 
 describe("klDivergence: continuous -> continuous -> float", () => {
-  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
-
   let testUniform = (lowAnswer, highAnswer, lowPrediction, highPrediction) => {
     test("of two uniforms is equal to the analytic expression", () => {
       let answer =
@@ -58,7 +57,7 @@ describe("klDivergence: continuous -> continuous -> float", () => {
     let kl = E.R.liftJoin2(klDivergence, prediction, answer)
 
     switch kl {
-    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=3)
+    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=2)
     | Error(err) => {
         Js.Console.log(DistributionTypes.Error.toString(err))
         raise(KlFailed)
@@ -82,7 +81,6 @@ describe("klDivergence: continuous -> continuous -> float", () => {
 })
 
 describe("klDivergence: discrete -> discrete -> float", () => {
-  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
   let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
   let a' = [(point1, 1e0), (point2, 1e0)]->mixture->run
   let b' = [(point1, 1e0), (point2, 1e0), (point3, 1e0)]->mixture->run
@@ -117,7 +115,6 @@ describe("klDivergence: discrete -> discrete -> float", () => {
 })
 
 describe("klDivergence: mixed -> mixed -> float", () => {
-  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
   let mixture' = a => DistributionTypes.DistributionOperation.Mixture(a)
   let mixture = a => {
     let dist' = a->mixture'->run
@@ -189,15 +186,15 @@ describe("combineAlongSupportOfSecondArgument0", () => {
       uniformMakeR(lowPrediction, highPrediction)->E.R2.errMap(s => DistributionTypes.ArgumentError(
         s,
       ))
-    let answerWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), answer)
+    let answerWrapped = E.R.fmap(a => run(FromDist(#ToDist(ToPointSet), a)), answer)
-    let predictionWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), prediction)
+    let predictionWrapped = E.R.fmap(a => run(FromDist(#ToDist(ToPointSet), a)), prediction)
 
     let interpolator = XYShape.XtoY.continuousInterpolator(#Stepwise, #UseZero)
-    let integrand = PointSetDist_Scoring.KLDivergence.integrand
+    let integrand = PointSetDist_Scoring.WithDistAnswer.integrand
 
     let result = switch (answerWrapped, predictionWrapped) {
     | (Ok(Dist(PointSet(Continuous(a)))), Ok(Dist(PointSet(Continuous(b))))) =>
-      Some(combineAlongSupportOfSecondArgument(integrand, interpolator, a.xyShape, b.xyShape))
+      Some(combineAlongSupportOfSecondArgument(interpolator, integrand, a.xyShape, b.xyShape))
     | _ => None
     }
     result

packages/squiggle-lang/__tests__/Distributions/Scoring/WithScalarAnswer_test.res

@@ -0,0 +1,68 @@
+open Jest
+open Expect
+open TestHelpers
+open GenericDist_Fixtures
+exception ScoreFailed
+
+describe("WithScalarAnswer: discrete -> scalar -> score", () => {
+  let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
+  let pointA = mkDelta(3.0)
+  let pointB = mkDelta(2.0)
+  let pointC = mkDelta(1.0)
+  let pointD = mkDelta(0.0)
+
+  test("score: agrees with analytical answer when finite", () => {
+    let prediction' = [(pointA, 0.25), (pointB, 0.25), (pointC, 0.25), (pointD, 0.25)]->mixture->run
+    let prediction = switch prediction' {
+    | Dist(PointSet(p)) => p
+    | _ => raise(MixtureFailed)
+    }
+
+    let answer = 2.0 // So this is: assigning 100% probability to 2.0
+    let result = PointSetDist_Scoring.WithScalarAnswer.score(~estimate=prediction, ~answer)
+    switch result {
+    | Ok(x) => x->expect->toEqual(-.Js.Math.log(0.25 /. 1.0))
+    | _ => raise(ScoreFailed)
+    }
+  })
+
+  test("score: agrees with analytical answer when finite", () => {
+    let prediction' = [(pointA, 0.75), (pointB, 0.25)]->mixture->run
+    let prediction = switch prediction' {
+    | Dist(PointSet(p)) => p
+    | _ => raise(MixtureFailed)
+    }
+    let answer = 3.0 // So this is: assigning 100% probability to 2.0
+    let result = PointSetDist_Scoring.WithScalarAnswer.score(~estimate=prediction, ~answer)
+    switch result {
+    | Ok(x) => x->expect->toEqual(-.Js.Math.log(0.75 /. 1.0))
+    | _ => raise(ScoreFailed)
+    }
+  })
+
+  test("scoreWithPrior: agrees with analytical answer when finite", () => {
+    let prior' = [(pointA, 0.5), (pointB, 0.5)]->mixture->run
+    let prediction' = [(pointA, 0.75), (pointB, 0.25)]->mixture->run
+
+    let prediction = switch prediction' {
+    | Dist(PointSet(p)) => p
+    | _ => raise(MixtureFailed)
+    }
+
+    let prior = switch prior' {
+    | Dist(PointSet(p)) => p
+    | _ => raise(MixtureFailed)
+    }
+
+    let answer = 3.0 // So this is: assigning 100% probability to 2.0
+    let result = PointSetDist_Scoring.WithScalarAnswer.scoreWithPrior(
+      ~estimate=prediction,
+      ~answer,
+      ~prior,
+    )
+    switch result {
+    | Ok(x) => x->expect->toEqual(-.Js.Math.log(0.75 /. 1.0) -. -.Js.Math.log(0.5 /. 1.0))
+    | _ => raise(ScoreFailed)
+    }
+  })
+})

packages/squiggle-lang/__tests__/Distributions/Symbolic_test.res

@@ -8,34 +8,34 @@ let mkNormal = (mean, stdev) => DistributionTypes.Symbolic(#Normal({mean: mean,
 describe("(Symbolic) normalize", () => {
   testAll("has no impact on normal distributions", list{-1e8, -1e-2, 0.0, 1e-4, 1e16}, mean => {
     let normalValue = mkNormal(mean, 2.0)
-    let normalizedValue = run(FromDist(ToDist(Normalize), normalValue))
+    let normalizedValue = run(FromDist(#ToDist(Normalize), normalValue))
     normalizedValue->unpackDist->expect->toEqual(normalValue)
   })
 })
 
 describe("(Symbolic) mean", () => {
   testAll("of normal distributions", list{-1e8, -16.0, -1e-2, 0.0, 1e-4, 32.0, 1e16}, mean => {
-    run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
+    run(FromDist(#ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
   })
 
   Skip.test("of normal(0, -1) (it NaNs out)", () => {
-    run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
+    run(FromDist(#ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
   })
 
   test("of normal(0, 1e-8) (it doesn't freak out at tiny stdev)", () => {
-    run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
+    run(FromDist(#ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
   })
 
   testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
     let meanValue = run(
-      FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
+      FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
     )
     meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. rate) // https://en.wikipedia.org/wiki/Exponential_distribution#Mean,_variance,_moments,_and_median
   })
 
   test("of a cauchy distribution", () => {
     let meanValue = run(
-      FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
+      FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
     )
     meanValue->unpackFloat->expect->toBeSoCloseTo(1.0098094001641797, ~digits=5)
     //-> toBe(GenDistError(Other("Cauchy distributions may have no mean value.")))
@@ -48,7 +48,7 @@ describe("(Symbolic) mean", () => {
       let (low, medium, high) = tup
       let meanValue = run(
         FromDist(
-          ToFloat(#Mean),
+          #ToFloat(#Mean),
           DistributionTypes.Symbolic(#Triangular({low: low, medium: medium, high: high})),
         ),
       )
@@ -63,7 +63,7 @@ describe("(Symbolic) mean", () => {
     tup => {
       let (alpha, beta) = tup
       let meanValue = run(
-        FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
+        FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
       )
       meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. (1.0 +. beta /. alpha)) // https://en.wikipedia.org/wiki/Beta_distribution#Mean
     },
@@ -72,7 +72,7 @@ describe("(Symbolic) mean", () => {
   // TODO: When we have our theory of validators we won't want this to be NaN but to be an error.
   test("of beta(0, 0)", () => {
     let meanValue = run(
-      FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
+      FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
     )
     meanValue->unpackFloat->expect->ExpectJs.toBeFalsy
   })
@@ -85,7 +85,7 @@ describe("(Symbolic) mean", () => {
       let betaDistribution = SymbolicDist.Beta.fromMeanAndStdev(mean, stdev)
       let meanValue =
         betaDistribution->E.R2.fmap(d =>
-          run(FromDist(ToFloat(#Mean), d->DistributionTypes.Symbolic))
+          run(FromDist(#ToFloat(#Mean), d->DistributionTypes.Symbolic))
         )
       switch meanValue {
       | Ok(value) => value->unpackFloat->expect->toBeCloseTo(mean)
@@ -100,7 +100,7 @@ describe("(Symbolic) mean", () => {
     tup => {
       let (mu, sigma) = tup
       let meanValue = run(
-        FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
+        FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
       )
       meanValue->unpackFloat->expect->toBeCloseTo(Js.Math.exp(mu +. sigma ** 2.0 /. 2.0)) // https://brilliant.org/wiki/log-normal-distribution/
     },
@@ -112,14 +112,14 @@ describe("(Symbolic) mean", () => {
     tup => {
       let (low, high) = tup
       let meanValue = run(
-        FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
+        FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
       )
       meanValue->unpackFloat->expect->toBeCloseTo((low +. high) /. 2.0) // https://en.wikipedia.org/wiki/Continuous_uniform_distribution#Moments
     },
   )
 
   test("of a float", () => {
-    let meanValue = run(FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
+    let meanValue = run(FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
     meanValue->unpackFloat->expect->toBeCloseTo(7.7)
   })
 })

packages/squiggle-lang/__tests__/TestHelpers.res

@@ -29,7 +29,7 @@ let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Ou
 
 let fnImage = (theFn, inps) => Js.Array.map(theFn, inps)
 
-let env: DistributionOperation.env = {
+let env: GenericDist.env = {
   sampleCount: MagicNumbers.Environment.defaultSampleCount,
   xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
 }

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

@@ -4,12 +4,9 @@ type error = DistributionTypes.error
 
 // TODO: It could be great to use a cache for some calculations (basically, do memoization). Also, better analytics/tracking could go a long way.
 
-type env = {
+type env = GenericDist.env
-  sampleCount: int,
-  xyPointLength: int,
-}
 
-let defaultEnv = {
+let defaultEnv: env = {
   sampleCount: MagicNumbers.Environment.defaultSampleCount,
   xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
 }
@@ -93,7 +90,7 @@ module OutputLocal = {
     }
 }
 
-let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
+let rec run = (~env: env, functionCallInfo: functionCallInfo): outputType => {
   let {sampleCount, xyPointLength} = env
 
   let reCall = (~env=env, ~functionCallInfo=functionCallInfo, ()) => {
@@ -101,14 +98,14 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
   }
 
   let toPointSetFn = r => {
-    switch reCall(~functionCallInfo=FromDist(ToDist(ToPointSet), r), ()) {
+    switch reCall(~functionCallInfo=FromDist(#ToDist(ToPointSet), r), ()) {
     | Dist(PointSet(p)) => Ok(p)
     | e => Error(OutputLocal.toErrorOrUnreachable(e))
     }
   }
 
   let toSampleSetFn = r => {
-    switch reCall(~functionCallInfo=FromDist(ToDist(ToSampleSet(sampleCount)), r), ()) {
+    switch reCall(~functionCallInfo=FromDist(#ToDist(ToSampleSet(sampleCount)), r), ()) {
     | Dist(SampleSet(p)) => Ok(p)
     | e => Error(OutputLocal.toErrorOrUnreachable(e))
     }
@@ -116,13 +113,13 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
 
   let scaleMultiply = (r, weight) =>
     reCall(
-      ~functionCallInfo=FromDist(ToDistCombination(Pointwise, #Multiply, #Float(weight)), r),
+      ~functionCallInfo=FromDist(#ToDistCombination(Pointwise, #Multiply, #Float(weight)), r),
       (),
     )->OutputLocal.toDistR
 
   let pointwiseAdd = (r1, r2) =>
     reCall(
-      ~functionCallInfo=FromDist(ToDistCombination(Pointwise, #Add, #Dist(r2)), r1),
+      ~functionCallInfo=FromDist(#ToDistCombination(Pointwise, #Add, #Dist(r2)), r1),
       (),
     )->OutputLocal.toDistR
 
@@ -131,49 +128,40 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
     dist: genericDist,
   ): outputType => {
     let response = switch subFnName {
-    | ToFloat(distToFloatOperation) =>
+    | #ToFloat(distToFloatOperation) =>
       GenericDist.toFloatOperation(dist, ~toPointSetFn, ~distToFloatOperation)
       ->E.R2.fmap(r => Float(r))
       ->OutputLocal.fromResult
-    | ToString(ToString) => dist->GenericDist.toString->String
+    | #ToString(ToString) => dist->GenericDist.toString->String
-    | ToString(ToSparkline(bucketCount)) =>
+    | #ToString(ToSparkline(bucketCount)) =>
       GenericDist.toSparkline(dist, ~sampleCount, ~bucketCount, ())
       ->E.R2.fmap(r => String(r))
       ->OutputLocal.fromResult
-    | ToDist(Inspect) => {
+    | #ToDist(Inspect) => {
         Js.log2("Console log requested: ", dist)
         Dist(dist)
       }
-    | ToDist(Normalize) => dist->GenericDist.normalize->Dist
+    | #ToDist(Normalize) => dist->GenericDist.normalize->Dist
-    | ToScore(KLDivergence(t2)) =>
+    | #ToScore(LogScore(answer, prior)) =>
-      GenericDist.Score.klDivergence(dist, t2, ~toPointSetFn)
+      GenericDist.Score.logScore(~estimate=dist, ~answer, ~prior, ~env)
-      ->E.R2.fmap(r => Float(r))
+      ->E.R2.fmap(s => Float(s))
       ->OutputLocal.fromResult
-    | ToScore(LogScore(answer, prior)) =>
+    | #ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
-      GenericDist.Score.logScoreWithPointResolution(
+    | #ToDist(Truncate(leftCutoff, rightCutoff)) =>
-        ~prediction=dist,
-        ~answer,
-        ~prior,
-        ~toPointSetFn,
-      )
-      ->E.R2.fmap(r => Float(r))
-      ->OutputLocal.fromResult
-    | ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
-    | ToDist(Truncate(leftCutoff, rightCutoff)) =>
       GenericDist.truncate(~toPointSetFn, ~leftCutoff, ~rightCutoff, dist, ())
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(ToSampleSet(n)) =>
+    | #ToDist(ToSampleSet(n)) =>
       dist
       ->GenericDist.toSampleSetDist(n)
       ->E.R2.fmap(r => Dist(SampleSet(r)))
       ->OutputLocal.fromResult
-    | ToDist(ToPointSet) =>
+    | #ToDist(ToPointSet) =>
       dist
       ->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
       ->E.R2.fmap(r => Dist(PointSet(r)))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
+    | #ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(
         ~toPointSetFn,
@@ -182,23 +170,23 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       )
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#Multiply, f)) =>
+    | #ToDist(Scale(#Multiply, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Multiply, ~f)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#Logarithm, f)) =>
+    | #ToDist(Scale(#Logarithm, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Logarithm, ~f)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDist(Scale(#Power, f)) =>
+    | #ToDist(Scale(#Power, f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Power, ~f)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDistCombination(Algebraic(_), _, #Float(_)) => GenDistError(NotYetImplemented)
+    | #ToDistCombination(Algebraic(_), _, #Float(_)) => GenDistError(NotYetImplemented)
-    | ToDistCombination(Algebraic(strategy), arithmeticOperation, #Dist(t2)) =>
+    | #ToDistCombination(Algebraic(strategy), arithmeticOperation, #Dist(t2)) =>
       dist
       ->GenericDist.algebraicCombination(
         ~strategy,
@@ -209,12 +197,12 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       )
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDistCombination(Pointwise, algebraicCombination, #Dist(t2)) =>
+    | #ToDistCombination(Pointwise, algebraicCombination, #Dist(t2)) =>
       dist
       ->GenericDist.pointwiseCombination(~toPointSetFn, ~algebraicCombination, ~t2)
       ->E.R2.fmap(r => Dist(r))
       ->OutputLocal.fromResult
-    | ToDistCombination(Pointwise, algebraicCombination, #Float(f)) =>
+    | #ToDistCombination(Pointwise, algebraicCombination, #Float(f)) =>
       dist
       ->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination, ~f)
       ->E.R2.fmap(r => Dist(r))
@@ -225,8 +213,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
 
   switch functionCallInfo {
   | FromDist(subFnName, dist) => fromDistFn(subFnName, dist)
-  | FromFloat(subFnName, float) =>
+  | FromFloat(subFnName, x) => reCall(~functionCallInfo=FromFloat(subFnName, x), ())
-    reCall(~functionCallInfo=FromDist(subFnName, GenericDist.fromFloat(float)), ())
   | Mixture(dists) =>
     dists
     ->GenericDist.mixture(~scaleMultiplyFn=scaleMultiply, ~pointwiseAddFn=pointwiseAdd)
@@ -278,13 +265,16 @@ module Constructors = {
   let pdf = (~env, dist, f) => C.pdf(dist, f)->run(~env)->toFloatR
   let normalize = (~env, dist) => C.normalize(dist)->run(~env)->toDistR
   let isNormalized = (~env, dist) => C.isNormalized(dist)->run(~env)->toBoolR
-  let klDivergence = (~env, dist1, dist2) => C.klDivergence(dist1, dist2)->run(~env)->toFloatR
+  module LogScore = {
-  let logScoreWithPointResolution = (
+    let distEstimateDistAnswer = (~env, estimate, answer) =>
-    ~env,
+      C.LogScore.distEstimateDistAnswer(estimate, answer)->run(~env)->toFloatR
-    ~prediction: DistributionTypes.genericDist,
+    let distEstimateDistAnswerWithPrior = (~env, estimate, answer, prior) =>
-    ~answer: float,
+      C.LogScore.distEstimateDistAnswerWithPrior(estimate, answer, prior)->run(~env)->toFloatR
-    ~prior: option<DistributionTypes.genericDist>,
+    let distEstimateScalarAnswer = (~env, estimate, answer) =>
-  ) => C.logScoreWithPointResolution(~prediction, ~answer, ~prior)->run(~env)->toFloatR
+      C.LogScore.distEstimateScalarAnswer(estimate, answer)->run(~env)->toFloatR
+    let distEstimateScalarAnswerWithPrior = (~env, estimate, answer, prior) =>
+      C.LogScore.distEstimateScalarAnswerWithPrior(estimate, answer, prior)->run(~env)->toFloatR
+  }
   let toPointSet = (~env, dist) => C.toPointSet(dist)->run(~env)->toDistR
   let toSampleSet = (~env, dist, n) => C.toSampleSet(dist, n)->run(~env)->toDistR
   let fromSamples = (~env, xs) => C.fromSamples(xs)->run(~env)->toDistR

packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.resi

@@ -1,11 +1,5 @@
 @genType
-type env = {
+let defaultEnv: GenericDist.env
-  sampleCount: int,
-  xyPointLength: int,
-}
-
-@genType
-let defaultEnv: env
 
 open DistributionTypes
 
@@ -19,15 +13,18 @@ type outputType =
   | GenDistError(error)
 
 @genType
-let run: (~env: env, DistributionTypes.DistributionOperation.genericFunctionCallInfo) => outputType
+let run: (
+  ~env: GenericDist.env,
+  DistributionTypes.DistributionOperation.genericFunctionCallInfo,
+) => outputType
 let runFromDist: (
-  ~env: env,
+  ~env: GenericDist.env,
   ~functionCallInfo: DistributionTypes.DistributionOperation.fromDist,
   genericDist,
 ) => outputType
 let runFromFloat: (
-  ~env: env,
+  ~env: GenericDist.env,
-  ~functionCallInfo: DistributionTypes.DistributionOperation.fromDist,
+  ~functionCallInfo: DistributionTypes.DistributionOperation.fromFloat,
   float,
 ) => outputType
 
@@ -42,79 +39,147 @@ module Output: {
   let toBool: t => option<bool>
   let toBoolR: t => result<bool, error>
   let toError: t => option<error>
-  let fmap: (~env: env, t, DistributionTypes.DistributionOperation.singleParamaterFunction) => t
+  let fmap: (
+    ~env: GenericDist.env,
+    t,
+    DistributionTypes.DistributionOperation.singleParamaterFunction,
+  ) => t
 }
 
 module Constructors: {
   @genType
-  let mean: (~env: env, genericDist) => result<float, error>
+  let mean: (~env: GenericDist.env, genericDist) => result<float, error>
   @genType
-  let stdev: (~env: env, genericDist) => result<float, error>
+  let stdev: (~env: GenericDist.env, genericDist) => result<float, error>
   @genType
-  let variance: (~env: env, genericDist) => result<float, error>
+  let variance: (~env: GenericDist.env, genericDist) => result<float, error>
   @genType
-  let sample: (~env: env, genericDist) => result<float, error>
+  let sample: (~env: GenericDist.env, genericDist) => result<float, error>
   @genType
-  let cdf: (~env: env, genericDist, float) => result<float, error>
+  let cdf: (~env: GenericDist.env, genericDist, float) => result<float, error>
   @genType
-  let inv: (~env: env, genericDist, float) => result<float, error>
+  let inv: (~env: GenericDist.env, genericDist, float) => result<float, error>
   @genType
-  let pdf: (~env: env, genericDist, float) => result<float, error>
+  let pdf: (~env: GenericDist.env, genericDist, float) => result<float, error>
   @genType
-  let normalize: (~env: env, genericDist) => result<genericDist, error>
+  let normalize: (~env: GenericDist.env, genericDist) => result<genericDist, error>
   @genType
-  let isNormalized: (~env: env, genericDist) => result<bool, error>
+  let isNormalized: (~env: GenericDist.env, genericDist) => result<bool, error>
+  module LogScore: {
+    @genType
+    let distEstimateDistAnswer: (
+      ~env: GenericDist.env,
+      genericDist,
+      genericDist,
+    ) => result<float, error>
+    @genType
+    let distEstimateDistAnswerWithPrior: (
+      ~env: GenericDist.env,
+      genericDist,
+      genericDist,
+      genericDist,
+    ) => result<float, error>
+    @genType
+    let distEstimateScalarAnswer: (
+      ~env: GenericDist.env,
+      genericDist,
+      float,
+    ) => result<float, error>
+    @genType
+    let distEstimateScalarAnswerWithPrior: (
+      ~env: GenericDist.env,
+      genericDist,
+      float,
+      genericDist,
+    ) => result<float, error>
+  }
   @genType
-  let klDivergence: (~env: env, genericDist, genericDist) => result<float, error>
+  let toPointSet: (~env: GenericDist.env, genericDist) => result<genericDist, error>
   @genType
-  let logScoreWithPointResolution: (
+  let toSampleSet: (~env: GenericDist.env, genericDist, int) => result<genericDist, error>
-    ~env: env,
-    ~prediction: genericDist,
-    ~answer: float,
-    ~prior: option<genericDist>,
-  ) => result<float, error>
   @genType
-  let toPointSet: (~env: env, genericDist) => result<genericDist, error>
+  let fromSamples: (~env: GenericDist.env, SampleSetDist.t) => result<genericDist, error>
   @genType
-  let toSampleSet: (~env: env, genericDist, int) => result<genericDist, error>
+  let truncate: (
+    ~env: GenericDist.env,
+    genericDist,
+    option<float>,
+    option<float>,
+  ) => result<genericDist, error>
   @genType
-  let fromSamples: (~env: env, SampleSetDist.t) => result<genericDist, error>
+  let inspect: (~env: GenericDist.env, genericDist) => result<genericDist, error>
   @genType
-  let truncate: (~env: env, genericDist, option<float>, option<float>) => result<genericDist, error>
+  let toString: (~env: GenericDist.env, genericDist) => result<string, error>
   @genType
-  let inspect: (~env: env, genericDist) => result<genericDist, error>
+  let toSparkline: (~env: GenericDist.env, genericDist, int) => result<string, error>
   @genType
-  let toString: (~env: env, genericDist) => result<string, error>
+  let algebraicAdd: (~env: GenericDist.env, genericDist, genericDist) => result<genericDist, error>
   @genType
-  let toSparkline: (~env: env, genericDist, int) => result<string, error>
+  let algebraicMultiply: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let algebraicAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let algebraicDivide: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let algebraicMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let algebraicSubtract: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let algebraicDivide: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let algebraicLogarithm: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let algebraicSubtract: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let algebraicPower: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let algebraicLogarithm: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let scaleLogarithm: (~env: GenericDist.env, genericDist, float) => result<genericDist, error>
   @genType
-  let algebraicPower: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let scaleMultiply: (~env: GenericDist.env, genericDist, float) => result<genericDist, error>
   @genType
-  let scaleLogarithm: (~env: env, genericDist, float) => result<genericDist, error>
+  let scalePower: (~env: GenericDist.env, genericDist, float) => result<genericDist, error>
   @genType
-  let scaleMultiply: (~env: env, genericDist, float) => result<genericDist, error>
+  let pointwiseAdd: (~env: GenericDist.env, genericDist, genericDist) => result<genericDist, error>
   @genType
-  let scalePower: (~env: env, genericDist, float) => result<genericDist, error>
+  let pointwiseMultiply: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let pointwiseAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let pointwiseDivide: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let pointwiseMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let pointwiseSubtract: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let pointwiseDivide: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let pointwiseLogarithm: (
+    ~env: GenericDist.env,
+    genericDist,
+    genericDist,
+  ) => result<genericDist, error>
   @genType
-  let pointwiseSubtract: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  let pointwisePower: (
-  @genType
+    ~env: GenericDist.env,
-  let pointwiseLogarithm: (~env: env, genericDist, genericDist) => result<genericDist, error>
+    genericDist,
-  @genType
+    genericDist,
-  let pointwisePower: (~env: env, genericDist, genericDist) => result<genericDist, error>
+  ) => result<genericDist, error>
 }

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

@@ -98,61 +98,86 @@ module DistributionOperation = {
     | ToString
     | ToSparkline(int)
 
-  type toScore = KLDivergence(genericDist) | LogScore(float, option<genericDist>)
+  type genericDistOrScalar = Score_Dist(genericDist) | Score_Scalar(float)
 
-  type fromDist =
+  type toScore = LogScore(genericDistOrScalar, option<genericDist>)
-    | ToFloat(toFloat)
+
-    | ToDist(toDist)
+  type fromFloat = [
-    | ToScore(toScore)
+    | #ToFloat(toFloat)
-    | ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
+    | #ToDist(toDist)
-    | ToString(toString)
+    | #ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
-    | ToBool(toBool)
+    | #ToString(toString)
+    | #ToBool(toBool)
+  ]
+
+  type fromDist = [
+    | fromFloat
+    | #ToScore(toScore)
+  ]
 
   type singleParamaterFunction =
     | FromDist(fromDist)
-    | FromFloat(fromDist)
+    | FromFloat(fromFloat)
 
   type genericFunctionCallInfo =
     | FromDist(fromDist, genericDist)
-    | FromFloat(fromDist, float)
+    | FromFloat(fromFloat, float)
     | FromSamples(array<float>)
     | Mixture(array<(genericDist, float)>)
 
-  let distCallToString = (distFunction: fromDist): string =>
+  let floatCallToString = (floatFunction: fromFloat): string =>
-    switch distFunction {
+    switch floatFunction {
-    | ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
+    | #ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
-    | ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
+    | #ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
-    | ToFloat(#Mean) => `mean`
+    | #ToFloat(#Mean) => `mean`
-    | ToFloat(#Min) => `min`
+    | #ToFloat(#Min) => `min`
-    | ToFloat(#Max) => `max`
+    | #ToFloat(#Max) => `max`
-    | ToFloat(#Stdev) => `stdev`
+    | #ToFloat(#Stdev) => `stdev`
-    | ToFloat(#Variance) => `variance`
+    | #ToFloat(#Variance) => `variance`
-    | ToFloat(#Mode) => `mode`
+    | #ToFloat(#Mode) => `mode`
-    | ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
+    | #ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
-    | ToFloat(#Sample) => `sample`
+    | #ToFloat(#Sample) => `sample`
-    | ToFloat(#IntegralSum) => `integralSum`
+    | #ToFloat(#IntegralSum) => `integralSum`
-    | ToScore(KLDivergence(_)) => `klDivergence`
+    | #ToDist(Normalize) => `normalize`
-    | ToScore(LogScore(x, _)) => `logScore against ${E.Float.toFixed(x)}`
+    | #ToDist(ToPointSet) => `toPointSet`
-    | ToDist(Normalize) => `normalize`
+    | #ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
-    | ToDist(ToPointSet) => `toPointSet`
+    | #ToDist(Truncate(_, _)) => `truncate`
-    | ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
+    | #ToDist(Inspect) => `inspect`
-    | ToDist(Truncate(_, _)) => `truncate`
+    | #ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
-    | ToDist(Inspect) => `inspect`
+    | #ToDist(Scale(#Multiply, r)) => `scaleMultiply(${E.Float.toFixed(r)})`
-    | ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
+    | #ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
-    | ToDist(Scale(#Multiply, r)) => `scaleMultiply(${E.Float.toFixed(r)})`
+    | #ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
-    | ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
-    | ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
       `scaleLogWithThreshold(${E.Float.toFixed(r)}, epsilon=${E.Float.toFixed(eps)})`
-    | ToString(ToString) => `toString`
+    | #ToString(ToString) => `toString`
-    | ToString(ToSparkline(n)) => `sparkline(${E.I.toString(n)})`
+    | #ToString(ToSparkline(n)) => `sparkline(${E.I.toString(n)})`
-    | ToBool(IsNormalized) => `isNormalized`
+    | #ToBool(IsNormalized) => `isNormalized`
-    | ToDistCombination(Algebraic(_), _, _) => `algebraic`
+    | #ToDistCombination(Algebraic(_), _, _) => `algebraic`
-    | ToDistCombination(Pointwise, _, _) => `pointwise`
+    | #ToDistCombination(Pointwise, _, _) => `pointwise`
+    }
+
+  let distCallToString = (
+    distFunction: [
+      | #ToFloat(toFloat)
+      | #ToDist(toDist)
+      | #ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
+      | #ToString(toString)
+      | #ToBool(toBool)
+      | #ToScore(toScore)
+    ],
+  ): string =>
+    switch distFunction {
+    | #ToScore(_) => `logScore`
+    | #ToFloat(x) => floatCallToString(#ToFloat(x))
+    | #ToDist(x) => floatCallToString(#ToDist(x))
+    | #ToString(x) => floatCallToString(#ToString(x))
+    | #ToBool(x) => floatCallToString(#ToBool(x))
+    | #ToDistCombination(x, y, z) => floatCallToString(#ToDistCombination(x, y, z))
     }
 
   let toString = (d: genericFunctionCallInfo): string =>
     switch d {
-    | FromDist(f, _) | FromFloat(f, _) => distCallToString(f)
+    | FromDist(f, _) => distCallToString(f)
+    | FromFloat(f, _) => floatCallToString(f)
     | Mixture(_) => `mixture`
     | FromSamples(_) => `fromSamples`
     }
@@ -162,80 +187,93 @@ module Constructors = {
 
   module UsingDists = {
     @genType
-    let mean = (dist): t => FromDist(ToFloat(#Mean), dist)
+    let mean = (dist): t => FromDist(#ToFloat(#Mean), dist)
-    let stdev = (dist): t => FromDist(ToFloat(#Stdev), dist)
+    let stdev = (dist): t => FromDist(#ToFloat(#Stdev), dist)
-    let variance = (dist): t => FromDist(ToFloat(#Variance), dist)
+    let variance = (dist): t => FromDist(#ToFloat(#Variance), dist)
-    let sample = (dist): t => FromDist(ToFloat(#Sample), dist)
+    let sample = (dist): t => FromDist(#ToFloat(#Sample), dist)
-    let cdf = (dist, x): t => FromDist(ToFloat(#Cdf(x)), dist)
+    let cdf = (dist, x): t => FromDist(#ToFloat(#Cdf(x)), dist)
-    let inv = (dist, x): t => FromDist(ToFloat(#Inv(x)), dist)
+    let inv = (dist, x): t => FromDist(#ToFloat(#Inv(x)), dist)
-    let pdf = (dist, x): t => FromDist(ToFloat(#Pdf(x)), dist)
+    let pdf = (dist, x): t => FromDist(#ToFloat(#Pdf(x)), dist)
-    let normalize = (dist): t => FromDist(ToDist(Normalize), dist)
+    let normalize = (dist): t => FromDist(#ToDist(Normalize), dist)
-    let isNormalized = (dist): t => FromDist(ToBool(IsNormalized), dist)
+    let isNormalized = (dist): t => FromDist(#ToBool(IsNormalized), dist)
-    let toPointSet = (dist): t => FromDist(ToDist(ToPointSet), dist)
+    let toPointSet = (dist): t => FromDist(#ToDist(ToPointSet), dist)
-    let toSampleSet = (dist, r): t => FromDist(ToDist(ToSampleSet(r)), dist)
+    let toSampleSet = (dist, r): t => FromDist(#ToDist(ToSampleSet(r)), dist)
     let fromSamples = (xs): t => FromSamples(xs)
-    let truncate = (dist, left, right): t => FromDist(ToDist(Truncate(left, right)), dist)
+    let truncate = (dist, left, right): t => FromDist(#ToDist(Truncate(left, right)), dist)
-    let inspect = (dist): t => FromDist(ToDist(Inspect), dist)
+    let inspect = (dist): t => FromDist(#ToDist(Inspect), dist)
-    let klDivergence = (dist1, dist2): t => FromDist(ToScore(KLDivergence(dist2)), dist1)
+    module LogScore = {
-    let logScoreWithPointResolution = (~prediction, ~answer, ~prior): t => FromDist(
+      let distEstimateDistAnswer = (estimate, answer): t => FromDist(
-      ToScore(LogScore(answer, prior)),
+        #ToScore(LogScore(Score_Dist(answer), None)),
-      prediction,
+        estimate,
-    )
+      )
-    let scaleMultiply = (dist, n): t => FromDist(ToDist(Scale(#Multiply, n)), dist)
+      let distEstimateDistAnswerWithPrior = (estimate, answer, prior): t => FromDist(
-    let scalePower = (dist, n): t => FromDist(ToDist(Scale(#Power, n)), dist)
+        #ToScore(LogScore(Score_Dist(answer), Some(prior))),
-    let scaleLogarithm = (dist, n): t => FromDist(ToDist(Scale(#Logarithm, n)), dist)
+        estimate,
+      )
+      let distEstimateScalarAnswer = (estimate, answer): t => FromDist(
+        #ToScore(LogScore(Score_Scalar(answer), None)),
+        estimate,
+      )
+      let distEstimateScalarAnswerWithPrior = (estimate, answer, prior): t => FromDist(
+        #ToScore(LogScore(Score_Scalar(answer), Some(prior))),
+        estimate,
+      )
+    }
+    let scaleMultiply = (dist, n): t => FromDist(#ToDist(Scale(#Multiply, n)), dist)
+    let scalePower = (dist, n): t => FromDist(#ToDist(Scale(#Power, n)), dist)
+    let scaleLogarithm = (dist, n): t => FromDist(#ToDist(Scale(#Logarithm, n)), dist)
     let scaleLogarithmWithThreshold = (dist, n, eps): t => FromDist(
-      ToDist(Scale(#LogarithmWithThreshold(eps), n)),
+      #ToDist(Scale(#LogarithmWithThreshold(eps), n)),
       dist,
     )
-    let toString = (dist): t => FromDist(ToString(ToString), dist)
+    let toString = (dist): t => FromDist(#ToString(ToString), dist)
-    let toSparkline = (dist, n): t => FromDist(ToString(ToSparkline(n)), dist)
+    let toSparkline = (dist, n): t => FromDist(#ToString(ToSparkline(n)), dist)
     let algebraicAdd = (dist1, dist2: genericDist): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Add, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Add, #Dist(dist2)),
       dist1,
     )
     let algebraicMultiply = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Multiply, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Multiply, #Dist(dist2)),
       dist1,
     )
     let algebraicDivide = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Divide, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Divide, #Dist(dist2)),
       dist1,
     )
     let algebraicSubtract = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Subtract, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Subtract, #Dist(dist2)),
       dist1,
     )
     let algebraicLogarithm = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Logarithm, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Logarithm, #Dist(dist2)),
       dist1,
     )
     let algebraicPower = (dist1, dist2): t => FromDist(
-      ToDistCombination(Algebraic(AsDefault), #Power, #Dist(dist2)),
+      #ToDistCombination(Algebraic(AsDefault), #Power, #Dist(dist2)),
       dist1,
     )
     let pointwiseAdd = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Add, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Add, #Dist(dist2)),
       dist1,
     )
     let pointwiseMultiply = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
       dist1,
     )
     let pointwiseDivide = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
       dist1,
     )
     let pointwiseSubtract = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
       dist1,
     )
     let pointwiseLogarithm = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
       dist1,
     )
     let pointwisePower = (dist1, dist2): t => FromDist(
-      ToDistCombination(Pointwise, #Power, #Dist(dist2)),
+      #ToDistCombination(Pointwise, #Power, #Dist(dist2)),
       dist1,
     )
   }

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

@@ -6,6 +6,11 @@ type toSampleSetFn = t => result<SampleSetDist.t, error>
 type scaleMultiplyFn = (t, float) => result<t, error>
 type pointwiseAddFn = (t, t) => result<t, error>
 
+type env = {
+  sampleCount: int,
+  xyPointLength: int,
+}
+
 let isPointSet = (t: t) =>
   switch t {
   | PointSet(_) => true
@@ -61,46 +66,6 @@ let integralEndY = (t: t): float =>
 
 let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
 
-module Score = {
-  let klDivergence = (prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
-    let pointSets = E.R.merge(toPointSetFn(prediction), toPointSetFn(answer))
-    pointSets |> E.R2.bind(((predi, ans)) =>
-      PointSetDist.T.klDivergence(predi, ans)->E.R2.errMap(x => DistributionTypes.OperationError(x))
-    )
-  }
-
-  let logScoreWithPointResolution = (
-    ~prediction: DistributionTypes.genericDist,
-    ~answer: float,
-    ~prior: option<DistributionTypes.genericDist>,
-    ~toPointSetFn: toPointSetFn,
-  ): result<float, error> => {
-    switch prior {
-    | Some(prior') =>
-      E.R.merge(toPointSetFn(prior'), toPointSetFn(prediction))->E.R.bind(((
-        prior'',
-        prediction'',
-      )) =>
-        PointSetDist.T.logScoreWithPointResolution(
-          ~prediction=prediction'',
-          ~answer,
-          ~prior=prior''->Some,
-        )->E.R2.errMap(x => DistributionTypes.OperationError(x))
-      )
-    | None =>
-      prediction
-      ->toPointSetFn
-      ->E.R.bind(x =>
-        PointSetDist.T.logScoreWithPointResolution(
-          ~prediction=x,
-          ~answer,
-          ~prior=None,
-        )->E.R2.errMap(x => DistributionTypes.OperationError(x))
-      )
-    }
-  }
-}
-
 let toFloatOperation = (
   t,
   ~toPointSetFn: toPointSetFn,
@@ -171,6 +136,70 @@ let toPointSet = (
   }
 }
 
+module Score = {
+  type genericDistOrScalar = DistributionTypes.DistributionOperation.genericDistOrScalar
+
+  let argsMake = (~esti: t, ~answ: genericDistOrScalar, ~prior: option<t>, ~env: env): result<
+    PointSetDist_Scoring.scoreArgs,
+    error,
+  > => {
+    let toPointSetFn = t =>
+      toPointSet(
+        t,
+        ~xyPointLength=env.xyPointLength,
+        ~sampleCount=env.sampleCount,
+        ~xSelection=#ByWeight,
+        (),
+      )
+    let prior': option<result<PointSetTypes.pointSetDist, error>> = switch prior {
+    | None => None
+    | Some(d) => toPointSetFn(d)->Some
+    }
+    let twoDists = (~toPointSetFn, esti': t, answ': t): result<
+      (PointSetTypes.pointSetDist, PointSetTypes.pointSetDist),
+      error,
+    > => E.R.merge(toPointSetFn(esti'), toPointSetFn(answ'))
+    switch (esti, answ, prior') {
+    | (esti', Score_Dist(answ'), None) =>
+      twoDists(~toPointSetFn, esti', answ')->E.R2.fmap(((esti'', answ'')) =>
+        {estimate: esti'', answer: answ'', prior: None}->PointSetDist_Scoring.DistAnswer
+      )
+    | (esti', Score_Dist(answ'), Some(Ok(prior''))) =>
+      twoDists(~toPointSetFn, esti', answ')->E.R2.fmap(((esti'', answ'')) =>
+        {
+          estimate: esti'',
+          answer: answ'',
+          prior: Some(prior''),
+        }->PointSetDist_Scoring.DistAnswer
+      )
+    | (esti', Score_Scalar(answ'), None) =>
+      toPointSetFn(esti')->E.R2.fmap(esti'' =>
+        {
+          estimate: esti'',
+          answer: answ',
+          prior: None,
+        }->PointSetDist_Scoring.ScalarAnswer
+      )
+    | (esti', Score_Scalar(answ'), Some(Ok(prior''))) =>
+      toPointSetFn(esti')->E.R2.fmap(esti'' =>
+        {
+          estimate: esti'',
+          answer: answ',
+          prior: Some(prior''),
+        }->PointSetDist_Scoring.ScalarAnswer
+      )
+    | (_, _, Some(Error(err))) => err->Error
+    }
+  }
+
+  let logScore = (~estimate: t, ~answer: genericDistOrScalar, ~prior: option<t>, ~env: env): result<
+    float,
+    error,
+  > =>
+    argsMake(~esti=estimate, ~answ=answer, ~prior, ~env)->E.R.bind(x =>
+      x->PointSetDist.logScore->E.R2.errMap(y => DistributionTypes.OperationError(y))
+    )
+}
 /*
   PointSetDist.toSparkline calls "downsampleEquallyOverX", which downsamples it to n=bucketCount.
   It first needs a pointSetDist, so we convert to a pointSetDist. In this process we want the 

packages/squiggle-lang/src/rescript/Distributions/GenericDist.resi

@@ -5,6 +5,9 @@ type toSampleSetFn = t => result<SampleSetDist.t, error>
 type scaleMultiplyFn = (t, float) => result<t, error>
 type pointwiseAddFn = (t, t) => result<t, error>
 
+@genType
+type env = {sampleCount: int, xyPointLength: int}
+
 let sampleN: (t, int) => array<float>
 let sample: t => float
 
@@ -25,12 +28,11 @@ let toFloatOperation: (
 ) => result<float, error>
 
 module Score: {
-  let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
+  let logScore: (
-  let logScoreWithPointResolution: (
+    ~estimate: t,
-    ~prediction: t,
+    ~answer: DistributionTypes.DistributionOperation.genericDistOrScalar,
-    ~answer: float,
     ~prior: option<t>,
-    ~toPointSetFn: toPointSetFn,
+    ~env: env,
   ) => result<float, error>
 }
 

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res

@@ -120,7 +120,7 @@ let combinePointwise = (
 
   let interpolator = XYShape.XtoY.continuousInterpolator(t1.interpolation, extrapolation)
 
-  combiner(fn, interpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(x =>
+  combiner(interpolator, fn, t1.xyShape, t2.xyShape)->E.R2.fmap(x =>
     make(~integralSumCache=combinedIntegralSum, x)
   )
 }
@@ -270,20 +270,6 @@ module T = Dist({
   }
   let variance = (t: t): float =>
     XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
-
-  let klDivergence = (prediction: t, answer: t) => {
-    let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
-      PointSetDist_Scoring.KLDivergence.integrand,
-      prediction.xyShape,
-      answer.xyShape,
-    )
-    newShape->E.R2.fmap(x => x->make->integralEndY)
-  }
-  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
-    let priorPdf = prior->E.O2.fmap((shape, x) => XYShape.XtoY.linear(x, shape.xyShape))
-    let predictionPdf = x => XYShape.XtoY.linear(x, prediction.xyShape)
-    PointSetDist_Scoring.LogScoreWithPointResolution.score(~priorPdf, ~predictionPdf, ~answer)
-  }
 })
 
 let isNormalized = (t: t): bool => {

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Discrete.res

@@ -49,7 +49,7 @@ let combinePointwise = (
   // TODO: does it ever make sense to pointwise combine the integrals here?
   // It could be done for pointwise additions, but is that ever needed?
 
-  combiner(fn, XYShape.XtoY.discreteInterpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(make)
+  combiner(XYShape.XtoY.discreteInterpolator, fn, t1.xyShape, t2.xyShape)->E.R2.fmap(make)
 }
 
 let reduce = (
@@ -222,15 +222,4 @@ module T = Dist({
     let getMeanOfSquares = t => t |> shapeMap(XYShape.T.square) |> mean
     XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
   }
-
-  let klDivergence = (prediction: t, answer: t) => {
-    combinePointwise(
-      ~fn=PointSetDist_Scoring.KLDivergence.integrand,
-      prediction,
-      answer,
-    )->E.R2.fmap(integralEndY)
-  }
-  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
-    Error(Operation.NotYetImplemented)
-  }
 })

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

@@ -33,12 +33,6 @@ module type dist = {
 
   let mean: t => float
   let variance: t => float
-  let klDivergence: (t, t) => result<float, Operation.Error.t>
-  let logScoreWithPointResolution: (
-    ~prediction: t,
-    ~answer: float,
-    ~prior: option<t>,
-  ) => result<float, Operation.Error.t>
 }
 
 module Dist = (T: dist) => {
@@ -61,9 +55,6 @@ module Dist = (T: dist) => {
   let mean = T.mean
   let variance = T.variance
   let integralEndY = T.integralEndY
-  let klDivergence = T.klDivergence
-  let logScoreWithPointResolution = T.logScoreWithPointResolution
-
   let updateIntegralCache = T.updateIntegralCache
 
   module Integral = {

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res

@@ -302,15 +302,6 @@ module T = Dist({
     | _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
     }
   }
-
-  let klDivergence = (prediction: t, answer: t) => {
-    let klDiscretePart = Discrete.T.klDivergence(prediction.discrete, answer.discrete)
-    let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
-    E.R.merge(klDiscretePart, klContinuousPart)->E.R2.fmap(t => fst(t) +. snd(t))
-  }
-  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
-    Error(Operation.NotYetImplemented)
-  }
 })
 
 let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

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

@@ -66,6 +66,7 @@ let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t
   }
 
 let combinePointwise = (
+  ~combiner=XYShape.PointwiseCombination.combine,
   ~integralSumCachesFn: (float, float) => option<float>=(_, _) => None,
   ~integralCachesFn: (
     PointSetTypes.continuousShape,
@@ -78,6 +79,7 @@ let combinePointwise = (
   switch (t1, t2) {
   | (Continuous(m1), Continuous(m2)) =>
     Continuous.combinePointwise(
+      ~combiner,
       ~integralSumCachesFn,
       fn,
       m1,
@@ -85,6 +87,7 @@ let combinePointwise = (
     )->E.R2.fmap(x => PointSetTypes.Continuous(x))
   | (Discrete(m1), Discrete(m2)) =>
     Discrete.combinePointwise(
+      ~combiner,
       ~integralSumCachesFn,
       ~fn,
       m1,
@@ -195,25 +198,16 @@ module T = Dist({
     | Discrete(m) => Discrete.T.variance(m)
     | Continuous(m) => Continuous.T.variance(m)
     }
-
-  let klDivergence = (prediction: t, answer: t) =>
-    switch (prediction, answer) {
-    | (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
-    | (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
-    | (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
-    }
-
-  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
-    switch (prior, prediction) {
-    | (Some(Continuous(t1)), Continuous(t2)) =>
-      Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=t1->Some)
-    | (None, Continuous(t2)) =>
-      Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=None)
-    | _ => Error(Operation.NotYetImplemented)
-    }
-  }
 })
 
+let logScore = (args: PointSetDist_Scoring.scoreArgs): result<float, Operation.Error.t> =>
+  PointSetDist_Scoring.logScore(
+    args,
+    ~combineFn=combinePointwise,
+    ~integrateFn=T.Integral.sum,
+    ~toMixedFn=toMixed,
+  )
+
 let pdf = (f: float, t: t) => {
   let mixedPoint: PointSetTypes.mixedPoint = T.xToY(f, t)
   mixedPoint.continuous +. mixedPoint.discrete

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist_Scoring.res

@@ -1,46 +1,149 @@
-module KLDivergence = {
+type pointSetDist = PointSetTypes.pointSetDist
-  let logFn = Js.Math.log // base e
+
-  let integrand = (predictionElement: float, answerElement: float): result<
+type scalar = float
+type score = float
+type abstractScoreArgs<'a, 'b> = {estimate: 'a, answer: 'b, prior: option<'a>}
+type scoreArgs =
+  | DistAnswer(abstractScoreArgs<pointSetDist, pointSetDist>)
+  | ScalarAnswer(abstractScoreArgs<pointSetDist, scalar>)
+
+let logFn = Js.Math.log // base e
+let minusScaledLogOfQuotient = (~esti, ~answ): result<float, Operation.Error.t> => {
+  let quot = esti /. answ
+  quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answ *. logFn(quot))
+}
+
+module WithDistAnswer = {
+  // The Kullback-Leibler divergence
+  let integrand = (estimateElement: float, answerElement: float): result<
     float,
     Operation.Error.t,
   > =>
-    // We decided that negative infinity, not an error at answerElement = 0.0, is a desirable value.
+    // We decided that 0.0, not an error at answerElement = 0.0, is a desirable value.
     if answerElement == 0.0 {
       Ok(0.0)
-    } else if predictionElement == 0.0 {
+    } else if estimateElement == 0.0 {
       Ok(infinity)
     } else {
-      let quot = predictionElement /. answerElement
+      minusScaledLogOfQuotient(~esti=estimateElement, ~answ=answerElement)
-      quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
     }
-}
 
-module LogScoreWithPointResolution = {
+  let sum = (
-  let logFn = Js.Math.log
+    ~estimate: pointSetDist,
-  let score = (
+    ~answer: pointSetDist,
-    ~priorPdf: option<float => float>,
+    ~combineFn,
-    ~predictionPdf: float => float,
+    ~integrateFn,
-    ~answer: float,
+    ~toMixedFn,
-  ): result<float, Operation.Error.t> => {
+  ): result<score, Operation.Error.t> => {
-    let numerator = answer->predictionPdf
+    let combineAndIntegrate = (estimate, answer) =>
-    if numerator < 0.0 {
+      combineFn(integrand, estimate, answer)->E.R2.fmap(integrateFn)
-      Operation.PdfInvalidError->Error
+
-    } else if numerator == 0.0 {
+    let getMixedSums = (estimate: pointSetDist, answer: pointSetDist) => {
-      infinity->Ok
+      let esti = estimate->toMixedFn
-    } else {
+      let answ = answer->toMixedFn
-      -.(
+      switch (
-        switch priorPdf {
+        Mixed.T.toContinuous(esti),
-        | None => numerator->logFn
+        Mixed.T.toDiscrete(esti),
-        | Some(f) => {
+        Mixed.T.toContinuous(answ),
-            let priorDensityOfAnswer = f(answer)
+        Mixed.T.toDiscrete(answ),
-            if priorDensityOfAnswer == 0.0 {
+      ) {
-              neg_infinity
+      | (
-            } else {
+          Some(estiContinuousPart),
-              (numerator /. priorDensityOfAnswer)->logFn
+          Some(estiDiscretePart),
-            }
+          Some(answContinuousPart),
-          }
+          Some(answDiscretePart),
-        }
+        ) =>
-      )->Ok
+        E.R.merge(
+          combineAndIntegrate(
+            PointSetTypes.Discrete(estiDiscretePart),
+            PointSetTypes.Discrete(answDiscretePart),
+          ),
+          combineAndIntegrate(Continuous(estiContinuousPart), Continuous(answContinuousPart)),
+        )
+      | (_, _, _, _) => `unreachable state`->Operation.Other->Error
+      }
+    }
+
+    switch (estimate, answer) {
+    | (Continuous(_), Continuous(_))
+    | (Discrete(_), Discrete(_)) =>
+      combineAndIntegrate(estimate, answer)
+    | (_, _) =>
+      getMixedSums(estimate, answer)->E.R2.fmap(((discretePart, continuousPart)) =>
+        discretePart +. continuousPart
+      )
     }
   }
+
+  let sumWithPrior = (
+    ~estimate: pointSetDist,
+    ~answer: pointSetDist,
+    ~prior: pointSetDist,
+    ~combineFn,
+    ~integrateFn,
+    ~toMixedFn,
+  ): result<score, Operation.Error.t> => {
+    let kl1 = sum(~estimate, ~answer, ~combineFn, ~integrateFn, ~toMixedFn)
+    let kl2 = sum(~estimate=prior, ~answer, ~combineFn, ~integrateFn, ~toMixedFn)
+    E.R.merge(kl1, kl2)->E.R2.fmap(((kl1', kl2')) => kl1' -. kl2')
+  }
 }
+
+module WithScalarAnswer = {
+  let sum = (mp: PointSetTypes.MixedPoint.t): float => mp.continuous +. mp.discrete
+  let score = (~estimate: pointSetDist, ~answer: scalar): result<score, Operation.Error.t> => {
+    let _score = (~estimatePdf: float => option<float>, ~answer: float): result<
+      score,
+      Operation.Error.t,
+    > => {
+      let density = answer->estimatePdf
+      switch density {
+      | None => Operation.PdfInvalidError->Error
+      | Some(density') =>
+        if density' < 0.0 {
+          Operation.PdfInvalidError->Error
+        } else if density' == 0.0 {
+          infinity->Ok
+        } else {
+          density'->logFn->(x => -.x)->Ok
+        }
+      }
+    }
+
+    let estimatePdf = x =>
+      switch estimate {
+      | Continuous(esti) => Continuous.T.xToY(x, esti)->sum->Some
+      | Discrete(esti) => Discrete.T.xToY(x, esti)->sum->Some
+      | Mixed(_) => None
+      }
+    _score(~estimatePdf, ~answer)
+  }
+
+  let scoreWithPrior = (~estimate: pointSetDist, ~answer: scalar, ~prior: pointSetDist): result<
+    score,
+    Operation.Error.t,
+  > => {
+    E.R.merge(score(~estimate, ~answer), score(~estimate=prior, ~answer))->E.R2.fmap(((s1, s2)) =>
+      s1 -. s2
+    )
+  }
+}
+
+let twoGenericDistsToTwoPointSetDists = (~toPointSetFn, estimate, answer): result<
+  (pointSetDist, pointSetDist),
+  'e,
+> => E.R.merge(toPointSetFn(estimate, ()), toPointSetFn(answer, ()))
+
+let logScore = (args: scoreArgs, ~combineFn, ~integrateFn, ~toMixedFn): result<
+  score,
+  Operation.Error.t,
+> =>
+  switch args {
+  | DistAnswer({estimate, answer, prior: None}) =>
+    WithDistAnswer.sum(~estimate, ~answer, ~integrateFn, ~combineFn, ~toMixedFn)
+  | DistAnswer({estimate, answer, prior: Some(prior)}) =>
+    WithDistAnswer.sumWithPrior(~estimate, ~answer, ~prior, ~integrateFn, ~combineFn, ~toMixedFn)
+  | ScalarAnswer({estimate, answer, prior: None}) => WithScalarAnswer.score(~estimate, ~answer)
+  | ScalarAnswer({estimate, answer, prior: Some(prior)}) =>
+    WithScalarAnswer.scoreWithPrior(~estimate, ~answer, ~prior)
+  }

packages/squiggle-lang/src/rescript/FunctionRegistry/FunctionRegistry_Core.res

@@ -8,6 +8,7 @@ type rec frType =
   | FRTypeNumber
   | FRTypeNumeric
   | FRTypeDistOrNumber
+  | FRTypeDist
   | FRTypeLambda
   | FRTypeRecord(frTypeRecord)
   | FRTypeDict(frType)
@@ -41,7 +42,7 @@ and frValueDistOrNumber = FRValueNumber(float) | FRValueDist(DistributionTypes.g
 type fnDefinition = {
   name: string,
   inputs: array<frType>,
-  run: (array<frValue>, DistributionOperation.env) => result<internalExpressionValue, string>,
+  run: (array<frValue>, GenericDist.env) => result<internalExpressionValue, string>,
 }
 
 type function = {
@@ -60,6 +61,7 @@ module FRType = {
     switch t {
     | FRTypeNumber => "number"
     | FRTypeNumeric => "numeric"
+    | FRTypeDist => "distribution"
     | FRTypeDistOrNumber => "distribution|number"
     | FRTypeRecord(r) => {
         let input = ((name, frType): frTypeRecordParam) => `${name}: ${toString(frType)}`
@@ -98,6 +100,7 @@ module FRType = {
     | (FRTypeDistOrNumber, IEvDistribution(Symbolic(#Float(f)))) =>
       Some(FRValueDistOrNumber(FRValueNumber(f)))
     | (FRTypeDistOrNumber, IEvDistribution(f)) => Some(FRValueDistOrNumber(FRValueDist(f)))
+    | (FRTypeDist, IEvDistribution(f)) => Some(FRValueDist(f))
     | (FRTypeNumeric, IEvNumber(f)) => Some(FRValueNumber(f))
     | (FRTypeNumeric, IEvDistribution(Symbolic(#Float(f)))) => Some(FRValueNumber(f))
     | (FRTypeLambda, IEvLambda(f)) => Some(FRValueLambda(f))
@@ -319,7 +322,7 @@ module FnDefinition = {
     t.name ++ `(${inputs})`
   }
 
-  let run = (t: t, args: array<internalExpressionValue>, env: DistributionOperation.env) => {
+  let run = (t: t, args: array<internalExpressionValue>, env: GenericDist.env) => {
     let argValues = FRType.matchWithExpressionValueArray(t.inputs, args)
     switch argValues {
     | Some(values) => t.run(values, env)
@@ -374,7 +377,7 @@ module Registry = {
     ~registry: registry,
     ~fnName: string,
     ~args: array<internalExpressionValue>,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ) => {
     let matchToDef = m => Matcher.Registry.matchToDef(registry, m)
     //Js.log(toSimple(registry))

packages/squiggle-lang/src/rescript/FunctionRegistry/FunctionRegistry_Helpers.res

@@ -27,6 +27,12 @@ module Prepare = {
         | _ => Error(impossibleError)
         }
 
+      let threeArgs = (inputs: ts): result<ts, err> =>
+        switch inputs {
+        | [FRValueRecord([(_, n1), (_, n2), (_, n3)])] => Ok([n1, n2, n3])
+        | _ => Error(impossibleError)
+        }
+
       let toArgs = (inputs: ts): result<ts, err> =>
         switch inputs {
         | [FRValueRecord(args)] => args->E.A2.fmap(((_, b)) => b)->Ok
@@ -57,6 +63,16 @@ module Prepare = {
       }
     }
 
+    let twoDist = (values: ts): result<
+      (DistributionTypes.genericDist, DistributionTypes.genericDist),
+      err,
+    > => {
+      switch values {
+      | [FRValueDist(a1), FRValueDist(a2)] => Ok(a1, a2)
+      | _ => Error(impossibleError)
+      }
+    }
+
     let twoNumbers = (values: ts): result<(float, float), err> => {
       switch values {
       | [FRValueNumber(a1), FRValueNumber(a2)] => Ok(a1, a2)
@@ -81,6 +97,11 @@ module Prepare = {
     module Record = {
       let twoDistOrNumber = (values: ts): result<(frValueDistOrNumber, frValueDistOrNumber), err> =>
         values->ToValueArray.Record.twoArgs->E.R.bind(twoDistOrNumber)
+
+      let twoDist = (values: ts): result<
+        (DistributionTypes.genericDist, DistributionTypes.genericDist),
+        err,
+      > => values->ToValueArray.Record.twoArgs->E.R.bind(twoDist)
     }
   }
 
@@ -128,8 +149,7 @@ module Prepare = {
 module Process = {
   module DistOrNumberToDist = {
     module Helpers = {
-      let toSampleSet = (r, env: DistributionOperation.env) =>
+      let toSampleSet = (r, env: GenericDist.env) => GenericDist.toSampleSetDist(r, env.sampleCount)
-        GenericDist.toSampleSetDist(r, env.sampleCount)
 
       let mapFnResult = r =>
         switch r {
@@ -166,7 +186,7 @@ module Process = {
     let oneValue = (
       ~fn: float => result<DistributionTypes.genericDist, string>,
       ~value: frValueDistOrNumber,
-      ~env: DistributionOperation.env,
+      ~env: GenericDist.env,
     ): result<DistributionTypes.genericDist, string> => {
       switch value {
       | FRValueNumber(a1) => fn(a1)
@@ -179,7 +199,7 @@ module Process = {
     let twoValues = (
       ~fn: ((float, float)) => result<DistributionTypes.genericDist, string>,
       ~values: (frValueDistOrNumber, frValueDistOrNumber),
-      ~env: DistributionOperation.env,
+      ~env: GenericDist.env,
     ): result<DistributionTypes.genericDist, string> => {
       switch values {
       | (FRValueNumber(a1), FRValueNumber(a2)) => fn((a1, a2))

packages/squiggle-lang/src/rescript/FunctionRegistry/FunctionRegistry_Library.res

@@ -49,7 +49,7 @@ let inputsTodist = (inputs: array<FunctionRegistry_Core.frValue>, makeDist) => {
   expressionValue
 }
 
-let registry = [
+let registryStart = [
   Function.make(
     ~name="toContinuousPointSet",
     ~definitions=[
@@ -510,3 +510,67 @@ to(5,10)
     (),
   ),
 ]
+
+let runScoring = (estimate, answer, prior, env) => {
+  GenericDist.Score.logScore(~estimate, ~answer, ~prior, ~env)
+  ->E.R2.fmap(FunctionRegistry_Helpers.Wrappers.evNumber)
+  ->E.R2.errMap(DistributionTypes.Error.toString)
+}
+
+let scoreFunctions = [
+  Function.make(
+    ~name="Score",
+    ~definitions=[
+      FnDefinition.make(
+        ~name="logScore",
+        ~inputs=[
+          FRTypeRecord([
+            ("estimate", FRTypeDist),
+            ("answer", FRTypeDistOrNumber),
+            ("prior", FRTypeDist),
+          ]),
+        ],
+        ~run=(inputs, env) => {
+          switch FunctionRegistry_Helpers.Prepare.ToValueArray.Record.threeArgs(inputs) {
+          | Ok([FRValueDist(estimate), FRValueDistOrNumber(FRValueDist(d)), FRValueDist(prior)]) =>
+            runScoring(estimate, Score_Dist(d), Some(prior), env)
+          | Ok([
+              FRValueDist(estimate),
+              FRValueDistOrNumber(FRValueNumber(d)),
+              FRValueDist(prior),
+            ]) =>
+            runScoring(estimate, Score_Scalar(d), Some(prior), env)
+          | Error(e) => Error(e)
+          | _ => Error(FunctionRegistry_Helpers.impossibleError)
+          }
+        },
+      ),
+      FnDefinition.make(
+        ~name="logScore",
+        ~inputs=[FRTypeRecord([("estimate", FRTypeDist), ("answer", FRTypeDistOrNumber)])],
+        ~run=(inputs, env) => {
+          switch FunctionRegistry_Helpers.Prepare.ToValueArray.Record.twoArgs(inputs) {
+          | Ok([FRValueDist(estimate), FRValueDistOrNumber(FRValueDist(d))]) =>
+            runScoring(estimate, Score_Dist(d), None, env)
+          | Ok([FRValueDist(estimate), FRValueDistOrNumber(FRValueNumber(d))]) =>
+            runScoring(estimate, Score_Scalar(d), None, env)
+          | Error(e) => Error(e)
+          | _ => Error(FunctionRegistry_Helpers.impossibleError)
+          }
+        },
+      ),
+      FnDefinition.make(~name="klDivergence", ~inputs=[FRTypeDist, FRTypeDist], ~run=(
+        inputs,
+        env,
+      ) => {
+        switch inputs {
+        | [FRValueDist(estimate), FRValueDist(d)] => runScoring(estimate, Score_Dist(d), None, env)
+        | _ => Error(FunctionRegistry_Helpers.impossibleError)
+        }
+      }),
+    ],
+    (),
+  ),
+]
+
+let registry = E.A.append(registryStart, scoreFunctions)

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

@@ -1,7 +1,7 @@
 module IEV = ReducerInterface_InternalExpressionValue
 type internalExpressionValue = IEV.t
 
-let dispatch = (call: IEV.functionCall, _: DistributionOperation.env): option<
+let dispatch = (call: IEV.functionCall, _: GenericDist.env): option<
   result<internalExpressionValue, QuriSquiggleLang.Reducer_ErrorValue.errorValue>,
 > => {
   switch call {

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

@@ -1,7 +1,7 @@
 module IEV = ReducerInterface_InternalExpressionValue
 type internalExpressionValue = IEV.t
 
-let dispatch = (call: IEV.functionCall, _: DistributionOperation.env): option<
+let dispatch = (call: IEV.functionCall, _: GenericDist.env): option<
   result<internalExpressionValue, QuriSquiggleLang.Reducer_ErrorValue.errorValue>,
 > => {
   switch call {

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

@@ -86,7 +86,7 @@ let toStringResult = x =>
   }
 
 @genType
-type environment = DistributionOperation.env
+type environment = GenericDist.env
 
 @genType
 let defaultEnvironment: environment = DistributionOperation.defaultEnv

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

@@ -32,50 +32,38 @@ module Helpers = {
   let toFloatFn = (
     fnCall: DistributionTypes.DistributionOperation.toFloat,
     dist: DistributionTypes.genericDist,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToFloat(fnCall), dist)
+    FromDist(#ToFloat(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let toStringFn = (
     fnCall: DistributionTypes.DistributionOperation.toString,
     dist: DistributionTypes.genericDist,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToString(fnCall), dist)
+    FromDist(#ToString(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let toBoolFn = (
     fnCall: DistributionTypes.DistributionOperation.toBool,
     dist: DistributionTypes.genericDist,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToBool(fnCall), dist)
+    FromDist(#ToBool(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
   let toDistFn = (
     fnCall: DistributionTypes.DistributionOperation.toDist,
     dist,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ) => {
-    FromDist(DistributionTypes.DistributionOperation.ToDist(fnCall), dist)
+    FromDist(#ToDist(fnCall), dist)->DistributionOperation.run(~env)->Some
-    ->DistributionOperation.run(~env)
-    ->Some
   }
 
-  let twoDiststoDistFn = (direction, arithmetic, dist1, dist2, ~env: DistributionOperation.env) => {
+  let twoDiststoDistFn = (direction, arithmetic, dist1, dist2, ~env: GenericDist.env) => {
     FromDist(
-      DistributionTypes.DistributionOperation.ToDistCombination(
+      #ToDistCombination(direction, arithmeticMap(arithmetic), #Dist(dist2)),
-        direction,
-        arithmeticMap(arithmetic),
-        #Dist(dist2),
-      ),
       dist1,
     )->DistributionOperation.run(~env)
   }
@@ -109,7 +97,7 @@ module Helpers = {
   let mixtureWithGivenWeights = (
     distributions: array<DistributionTypes.genericDist>,
     weights: array<float>,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ): DistributionOperation.outputType =>
     E.A.length(distributions) == E.A.length(weights)
       ? Mixture(Belt.Array.zip(distributions, weights))->DistributionOperation.run(~env)
@@ -119,7 +107,7 @@ module Helpers = {
 
   let mixtureWithDefaultWeights = (
     distributions: array<DistributionTypes.genericDist>,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ): DistributionOperation.outputType => {
     let length = E.A.length(distributions)
     let weights = Belt.Array.make(length, 1.0 /. Belt.Int.toFloat(length))
@@ -128,7 +116,7 @@ module Helpers = {
 
   let mixture = (
     args: array<internalExpressionValue>,
-    ~env: DistributionOperation.env,
+    ~env: GenericDist.env,
   ): DistributionOperation.outputType => {
     let error = (err: string): DistributionOperation.outputType =>
       err->DistributionTypes.ArgumentError->GenDistError
@@ -167,20 +155,6 @@ module Helpers = {
       }
     }
   }
-
-  let klDivergenceWithPrior = (
-    prediction: DistributionTypes.genericDist,
-    answer: DistributionTypes.genericDist,
-    prior: DistributionTypes.genericDist,
-    env: DistributionOperation.env,
-  ) => {
-    let term1 = DistributionOperation.Constructors.klDivergence(~env, prediction, answer)
-    let term2 = DistributionOperation.Constructors.klDivergence(~env, prior, answer)
-    switch E.R.merge(term1, term2)->E.R2.fmap(((a, b)) => a -. b) {
-    | Ok(x) => x->DistributionOperation.Float->Some
-    | Error(_) => None
-    }
-  }
 }
 
 module SymbolicConstructors = {
@@ -199,7 +173,7 @@ module SymbolicConstructors = {
     }
 }
 
-let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperation.env): option<
+let dispatchToGenericOutput = (call: IEV.functionCall, env: GenericDist.env): option<
   DistributionOperation.outputType,
 > => {
   let (fnName, args) = call
@@ -239,35 +213,6 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
       ~env,
     )->Some
   | ("normalize", [IEvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist, ~env)
-  | ("klDivergence", [IEvDistribution(prediction), IEvDistribution(answer)]) =>
-    Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(answer)), prediction), ~env))
-  | (
-      "klDivergence",
-      [IEvDistribution(prediction), IEvDistribution(answer), IEvDistribution(prior)],
-    ) =>
-    Helpers.klDivergenceWithPrior(prediction, answer, prior, env)
-  | (
-    "logScoreWithPointAnswer",
-    [IEvDistribution(prediction), IEvNumber(answer), IEvDistribution(prior)],
-  )
-  | (
-    "logScoreWithPointAnswer",
-    [
-      IEvDistribution(prediction),
-      IEvDistribution(Symbolic(#Float(answer))),
-      IEvDistribution(prior),
-    ],
-  ) =>
-    DistributionOperation.run(
-      FromDist(ToScore(LogScore(answer, prior->Some)), prediction),
-      ~env,
-    )->Some
-  | ("logScoreWithPointAnswer", [IEvDistribution(prediction), IEvNumber(answer)])
-  | (
-    "logScoreWithPointAnswer",
-    [IEvDistribution(prediction), IEvDistribution(Symbolic(#Float(answer)))],
-  ) =>
-    DistributionOperation.run(FromDist(ToScore(LogScore(answer, None)), prediction), ~env)->Some
   | ("isNormalized", [IEvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist, ~env)
   | ("toPointSet", [IEvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist, ~env)
   | ("scaleLog", [IEvDistribution(dist)]) =>

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

@@ -24,7 +24,7 @@ module ScientificUnit = {
   }
 }
 
-let dispatch = (call: IEV.functionCall, _: DistributionOperation.env): option<
+let dispatch = (call: IEV.functionCall, _: GenericDist.env): option<
   result<internalExpressionValue, QuriSquiggleLang.Reducer_ErrorValue.errorValue>,
 > => {
   switch call {

packages/squiggle-lang/src/rescript/TypescriptInterface.res

@@ -8,7 +8,7 @@ The below few seem to work fine. In the future there's definitely more work to d
 */
 
 @genType
-type samplingParams = DistributionOperation.env
+type samplingParams = GenericDist.env
 
 @genType
 type genericDist = DistributionTypes.genericDist

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

@@ -547,6 +547,7 @@ module A = {
   let init = Array.init
   let reduce = Belt.Array.reduce
   let reducei = Belt.Array.reduceWithIndex
+  let some = Belt.Array.some
   let isEmpty = r => length(r) < 1
   let stableSortBy = Belt.SortArray.stableSortBy
   let toNoneIfEmpty = r => isEmpty(r) ? None : Some(r)

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

@@ -327,8 +327,8 @@ module Zipped = {
 module PointwiseCombination = {
   // t1Interpolator and t2Interpolator are functions from XYShape.XtoY, e.g. linearBetweenPointsExtrapolateFlat.
   let combine: (
-    (float, float) => result<float, Operation.Error.t>,
     interpolator,
+    (float, float) => result<float, Operation.Error.t>,
     T.t,
     T.t,
   ) => result<T.t, Operation.Error.t> = %raw(`
@@ -337,7 +337,7 @@ module PointwiseCombination = {
       // and interpolates the value on the other side, thus accumulating xs and ys.
       // This is written in raw JS because this can still be a bottleneck, and using refs for the i and j indices is quite painful.
 
-      function(fn, interpolator, t1, t2) {
+      function(interpolator, fn, t1, t2) {
         let t1n = t1.xs.length;
         let t2n = t2.xs.length;
         let outX = [];
@@ -399,11 +399,11 @@ module PointwiseCombination = {
     This is from an approach to kl divergence that was ultimately rejected. Leaving it in for now because it may help us factor `combine` out of raw javascript soon.
  */
   let combineAlongSupportOfSecondArgument0: (
-    (float, float) => result<float, Operation.Error.t>,
     interpolator,
+    (float, float) => result<float, Operation.Error.t>,
     T.t,
     T.t,
-  ) => result<T.t, Operation.Error.t> = (fn, interpolator, t1, t2) => {
+  ) => result<T.t, Operation.Error.t> = (interpolator, fn, t1, t2) => {
     let newYs = []
     let newXs = []
     let (l1, l2) = (E.A.length(t1.xs), E.A.length(t2.xs))
@@ -496,29 +496,9 @@ module PointwiseCombination = {
     let newYs = E.A.fmap(x => XtoY.linear(x, t), newXs)
     {xs: newXs, ys: newYs}
   }
-  // This function is used for klDivergence
-  let combineAlongSupportOfSecondArgument: (
-    (float, float) => result<float, Operation.Error.t>,
-    T.t,
-    T.t,
-  ) => result<T.t, Operation.Error.t> = (fn, prediction, answer) => {
-    let combineWithFn = (answerX: float, i: int) => {
-      let answerY = answer.ys[i]
-      let predictionY = XtoY.linear(answerX, prediction)
-      fn(predictionY, answerY)
-    }
-    let newYsWithError = Js.Array.mapi((x, i) => combineWithFn(x, i), answer.xs)
-    let newYsOrError = E.A.R.firstErrorOrOpen(newYsWithError)
-    let result = switch newYsOrError {
-    | Ok(a) => Ok({xs: answer.xs, ys: a})
-    | Error(b) => Error(b)
-    }
-
-    result
-  }
 
   let addCombine = (interpolator: interpolator, t1: T.t, t2: T.t): T.t =>
-    combine((a, b) => Ok(a +. b), interpolator, t1, t2)->E.R.toExn(
+    combine(interpolator, (a, b) => Ok(a +. b), t1, t2)->E.R.toExn(
       "Add operation should never fail",
       _,
     )

packages/vscode-ext/README.md

@@ -1,21 +1,35 @@
 # Squiggle For VS Code
 
-_[marketplace](https://marketplace.visualstudio.com/items?itemName=QURI.vscode-squiggle)_
+## About
 
-This extension provides support for [Squiggle](https://www.squiggle-language.com/) in VS Code.
+This extension provides support for [Squiggle](https://www.squiggle-language.com/) in VS Code. It can be found in the VS code _[marketplace](https://marketplace.visualstudio.com/items?itemName=QURI.vscode-squiggle)_
 
 Features:
 
 - Preview `.squiggle` files in a preview pane
 - Syntax highlighting for `.squiggle` and `.squiggleU` files
 
-# Configuration
+## Installation 
 
-Some preview settings, e.g. whether to show the summary table or types of outputs, can be configurable on in the VS Code settings and persist between different preview sessions.
+You can install this extension by going to the "extensions" tab, searching for "Squiggle", and then installing it.
+
+![](./images/vs-code-install.png)
+
+## Usage
+
+After loading a `.squiggle` file, an "Open Preview" button will appear. If you click it, the squiggle model will be shown, and updated as you edit and save you file.
+
+![](./images/extension-screenshot.png)
+
+### Configuration (optional)
+
+Some preview settings, e.g. whether to show the summary table or types of outputs, can be configurable on in the VS Code settings and persist between different preview sessions. The VS Code settings can be accessed with the shortcut `Ctrl+,` with `Ctrl+Shift+P` + searching "Open Settings", or by accessing a file like `$HOME/.config/Code/User/settings.json` in Linux (see [here](https://stackoverflow.com/questions/65908987/how-can-i-open-visual-studio-codes-settings-json-file)) for other operating systems. 
+
+![](./images/vs-code-settings.png)
 
 Check out the full list of Squiggle settings in the main VS Code settings.
 
-# Build locally
+## Build locally
 
 We assume you ran `yarn` at the monorepo level for all dependencies.