diff --git a/.babelrc b/.babelrc
index 60cad8d..52b84f7 100644
--- a/.babelrc
+++ b/.babelrc
@@ -1,5 +1,7 @@
 {
+
   "presets": [
+
     ["@babel/env", {"modules": false}]
   ]
-}
\ No newline at end of file
+}
diff --git a/dist/d3-regression.cjs.js b/dist/d3-regression.cjs.js
index 8271f81..ea9f596 100644
--- a/dist/d3-regression.cjs.js
+++ b/dist/d3-regression.cjs.js
@@ -1,873 +1,764 @@
-// https://github.com/HarryStevens/d3-regression#readme Version 1.3.10. Copyright 2022 Harry Stevens.
+// https://github.com/HarryStevens/d3-regression#readme Version 1.3.10. Copyright 2025 Harry Stevens.
 'use strict';
 
 Object.defineProperty(exports, '__esModule', { value: true });
 
-function _slicedToArray(arr, i) {
-  return _arrayWithHoles(arr) || _iterableToArrayLimit(arr, i) || _nonIterableRest();
-}
-
-function _arrayWithHoles(arr) {
-  if (Array.isArray(arr)) return arr;
-}
-
-function _iterableToArrayLimit(arr, i) {
-  var _arr = [];
-  var _n = true;
-  var _d = false;
-  var _e = undefined;
-
-  try {
-    for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) {
-      _arr.push(_s.value);
-
-      if (i && _arr.length === i) break;
-    }
-  } catch (err) {
-    _d = true;
-    _e = err;
-  } finally {
-    try {
-      if (!_n && _i["return"] != null) _i["return"]();
-    } finally {
-      if (_d) throw _e;
-    }
-  }
-
-  return _arr;
-}
-
-function _nonIterableRest() {
-  throw new TypeError("Invalid attempt to destructure non-iterable instance");
-}
-
-// Adapted from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+/**
+ * Adapted from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+ */
 function points(data, x, y, sort) {
-  data = data.filter(function (d, i) {
-    var u = x(d, i),
-        v = y(d, i);
-    return u != null && isFinite(u) && v != null && isFinite(v);
-  });
-
-  if (sort) {
-    data.sort(function (a, b) {
-      return x(a) - x(b);
+    data = data.filter(function (d, i) {
+        var u = x(d, i), v = y(d, i);
+        return u != null && isFinite(u) && v != null && isFinite(v);
     });
-  }
-
-  var n = data.length,
-      X = new Float64Array(n),
-      Y = new Float64Array(n); // extract values, calculate means
-
-  var ux = 0,
-      uy = 0,
-      xv,
-      yv,
-      d;
-
-  for (var i = 0; i < n;) {
-    d = data[i];
-    X[i] = xv = +x(d, i, data);
-    Y[i] = yv = +y(d, i, data);
-    ++i;
-    ux += (xv - ux) / i;
-    uy += (yv - uy) / i;
-  } // mean center the data
-
-
-  for (var _i = 0; _i < n; ++_i) {
-    X[_i] -= ux;
-    Y[_i] -= uy;
-  }
-
-  return [X, Y, ux, uy];
+    if (sort) {
+        data.sort(function (a, b) { return x(a) - x(b); });
+    }
+    var n = data.length, X = new Float64Array(n), Y = new Float64Array(n);
+    // extract values, calculate means
+    var ux = 0, uy = 0, xv, yv, d;
+    for (var i = 0; i < n;) {
+        d = data[i];
+        X[i] = xv = +x(d, i, data);
+        Y[i] = yv = +y(d, i, data);
+        ++i;
+        ux += (xv - ux) / i;
+        uy += (yv - uy) / i;
+    }
+    // mean center the data
+    for (var i = 0; i < n; ++i) {
+        X[i] -= ux;
+        Y[i] -= uy;
+    }
+    return [X, Y, ux, uy];
 }
+/**
+ * Iterates over valid data points, invoking a callback for each.
+ */
 function visitPoints(data, x, y, cb) {
-  var iterations = 0;
-
-  for (var i = 0, n = data.length; i < n; i++) {
-    var d = data[i],
-        dx = +x(d, i, data),
-        dy = +y(d, i, data);
-
-    if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
-      cb(dx, dy, iterations++);
+    var iterations = 0;
+    for (var i = 0; i < data.length; i++) {
+        var d = data[i];
+        var dx = +x(d, i, data);
+        var dy = +y(d, i, data);
+        if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
+            cb(dx, dy, iterations++);
+        }
     }
-  }
 }
 
-// return the coefficient of determination, or R squared.
-
+/**
+ * Given a dataset, x- and y-accessors, the mean center of the y-values (uY),
+ * and a prediction function, return the coefficient of determination, R^2.
+ */
 function determination(data, x, y, uY, predict) {
-  var SSE = 0,
-      SST = 0;
-  visitPoints(data, x, y, function (dx, dy) {
-    var sse = dy - predict(dx),
-        sst = dy - uY;
-    SSE += sse * sse;
-    SST += sst * sst;
-  });
-  return 1 - SSE / SST;
+    var SSE = 0, // Sum of Squared Errors
+    SST = 0; // Total Sum of Squares
+    visitPoints(data, x, y, function (dx, dy) {
+        var sse = dy - predict(dx);
+        var sst = dy - uY;
+        SSE += sse * sse;
+        SST += sst * sst;
+    });
+    return 1 - SSE / SST;
 }
 
-// Returns the angle of a line in degrees.
+/**
+ * Returns the angle of a line in degrees.
+ */
 function angle(line) {
-  return Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) * 180 / Math.PI;
-} // Returns the midpoint of a line.
-
+    return (Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) *
+        (180 / Math.PI));
+}
+/**
+ * Returns the midpoint of a line.
+ */
 function midpoint(line) {
-  return [(line[0][0] + line[1][0]) / 2, (line[0][1] + line[1][1]) / 2];
+    return [
+        (line[0][0] + line[1][0]) / 2,
+        (line[0][1] + line[1][1]) / 2
+    ];
 }
 
-// returns a smooth line.
-
+/**
+ * Given a start point (xmin), an end point (xmax),
+ * and a prediction function, returns a smooth line.
+ */
 function interpose(xmin, xmax, predict) {
-  var l = Math.log(xmax - xmin) * Math.LOG10E + 1 | 0;
-  var precision = 1 * Math.pow(10, -l / 2 - 1),
-      maxIter = 1e4;
-  var points = [px(xmin), px(xmax)],
-      iter = 0;
-
-  while (find(points) && iter < maxIter) {
-  }
-
-  return points;
-
-  function px(x) {
-    return [x, predict(x)];
-  }
-
-  function find(points) {
-    iter++;
-    var n = points.length;
-    var found = false;
-
-    for (var i = 0; i < n - 1; i++) {
-      var p0 = points[i],
-          p1 = points[i + 1],
-          m = midpoint([p0, p1]),
-          mp = px(m[0]),
-          a0 = angle([p0, m]),
-          a1 = angle([p0, mp]),
-          a = Math.abs(a0 - a1);
-
-      if (a > precision) {
-        points.splice(i + 1, 0, mp);
-        found = true;
-      }
+    var l = (Math.log(xmax - xmin) * Math.LOG10E + 1) | 0;
+    var precision = Math.pow(10, -l / 2 - 1);
+    var maxIter = 1e4;
+    var points = [px(xmin), px(xmax)];
+    var iter = 0;
+    while (find(points) && iter < maxIter)
+        ;
+    return points;
+    function px(x) {
+        return [x, predict(x)];
+    }
+    function find(points) {
+        iter++;
+        var n = points.length;
+        var found = false;
+        for (var i = 0; i < n - 1; i++) {
+            var p0 = points[i];
+            var p1 = points[i + 1];
+            var m = midpoint([p0, p1]);
+            var mp = px(m[0]);
+            var a0 = angle([p0, m]);
+            var a1 = angle([p0, mp]);
+            var a = Math.abs(a0 - a1);
+            if (a > precision) {
+                points.splice(i + 1, 0, mp);
+                found = true;
+            }
+        }
+        return found;
     }
-
-    return found;
-  }
 }
 
-// Ordinary Least Squares from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+/**
+ * Ordinary Least Squares from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+ */
 function ols(uX, uY, uXY, uX2) {
-  var delta = uX2 - uX * uX,
-      slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta,
-      intercept = uY - slope * uX;
-  return [intercept, slope];
+    var delta = uX2 - uX * uX, slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta, intercept = uY - slope * uX;
+    return [intercept, slope];
 }
 
-function exponential () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function exponential(data) {
-    var n = 0,
-        Y = 0,
-        YL = 0,
-        XY = 0,
-        XYL = 0,
-        X2Y = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      var ly = Math.log(dy),
-          xy = dx * dy;
-      ++n;
-      Y += (dy - Y) / n;
-      XY += (xy - XY) / n;
-      X2Y += (dx * xy - X2Y) / n;
-      YL += (dy * ly - YL) / n;
-      XYL += (xy * ly - XYL) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y),
-        _ols2 = _slicedToArray(_ols, 2),
-        a = _ols2[0],
-        b = _ols2[1];
-
-    a = Math.exp(a);
-
-    var fn = function fn(x) {
-      return a * Math.exp(b * x);
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.a = a;
-    out.b = b;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  exponential.domain = function (arr) {
-    return arguments.length ? (domain = arr, exponential) : domain;
-  };
-
-  exponential.x = function (fn) {
-    return arguments.length ? (x = fn, exponential) : x;
-  };
-
-  exponential.y = function (fn) {
-    return arguments.length ? (y = fn, exponential) : y;
-  };
-
-  return exponential;
+function exponential() {
+    var y = function (d) { return d[1]; }, x = function (d) { return d[0]; }, domain;
+    var exponentialRegression = function (data) {
+        var n = 0, Y = 0, YL = 0, XY = 0, XYL = 0, X2Y = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            var ly = Math.log(dy), xy = dx * dy;
+            ++n;
+            Y += (dy - Y) / n;
+            XY += (xy - XY) / n;
+            X2Y += (dx * xy - X2Y) / n;
+            YL += (dy * ly - YL) / n;
+            XYL += (xy * ly - XYL) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y), a = _a[0], b = _a[1];
+        a = Math.exp(a);
+        var fn = function (xx) { return a * Math.exp(b * xx); };
+        var out = interpose(xmin, xmax, fn);
+        out.a = a;
+        out.b = b;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    exponentialRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return exponentialRegression;
+    };
+    exponentialRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return exponentialRegression;
+    };
+    exponentialRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return exponentialRegression;
+    };
+    return exponentialRegression;
 }
 
-function linear () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function linear(data) {
-    var n = 0,
-        X = 0,
-        // sum of x
-    Y = 0,
-        // sum of y
-    XY = 0,
-        // sum of x * y
-    X2 = 0,
-        // sum of x * x
-    xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      ++n;
-      X += (dx - X) / n;
-      Y += (dy - Y) / n;
-      XY += (dx * dy - XY) / n;
-      X2 += (dx * dx - X2) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(X, Y, XY, X2),
-        _ols2 = _slicedToArray(_ols, 2),
-        intercept = _ols2[0],
-        slope = _ols2[1],
-        fn = function fn(x) {
-      return slope * x + intercept;
-    },
-        out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
-
-    out.a = slope;
-    out.b = intercept;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  linear.domain = function (arr) {
-    return arguments.length ? (domain = arr, linear) : domain;
-  };
-
-  linear.x = function (fn) {
-    return arguments.length ? (x = fn, linear) : x;
-  };
-
-  linear.y = function (fn) {
-    return arguments.length ? (y = fn, linear) : y;
-  };
-
-  return linear;
+function linear() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+    var linearRegression = function (data) {
+        var n = 0, X = 0, // sum of x
+        Y = 0, // sum of y
+        XY = 0, // sum of x*y
+        X2 = 0, // sum of x*x
+        xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            ++n;
+            X += (dx - X) / n;
+            Y += (dy - Y) / n;
+            XY += (dx * dy - XY) / n;
+            X2 += (dx * dx - X2) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(X, Y, XY, X2), intercept = _a[0], slope = _a[1];
+        var fn = function (xx) { return slope * xx + intercept; };
+        var out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
+        out.a = slope;
+        out.b = intercept;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    linearRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return linearRegression;
+    };
+    linearRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return linearRegression;
+    };
+    linearRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return linearRegression;
+    };
+    return linearRegression;
 }
 
-// Returns the medium value of an array of numbers.
+/**
+ * Returns the median value of an array of numbers.
+ */
 function median(arr) {
-  arr.sort(function (a, b) {
-    return a - b;
-  });
-  var i = arr.length / 2;
-  return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
+    arr.sort(function (a, b) { return a - b; });
+    var i = arr.length / 2;
+    return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
 }
 
-var maxiters = 2,
-    epsilon = 1e-12;
-function loess () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      bandwidth = .3;
-
-  function loess(data) {
-    var _points = points(data, x, y, true),
-        _points2 = _slicedToArray(_points, 4),
-        xv = _points2[0],
-        yv = _points2[1],
-        ux = _points2[2],
-        uy = _points2[3],
-        n = xv.length,
-        bw = Math.max(2, ~~(bandwidth * n)),
-        yhat = new Float64Array(n),
-        residuals = new Float64Array(n),
-        robustWeights = new Float64Array(n).fill(1);
-
-    for (var iter = -1; ++iter <= maxiters;) {
-      var interval = [0, bw - 1];
-
-      for (var i = 0; i < n; ++i) {
-        var dx = xv[i],
-            i0 = interval[0],
-            i1 = interval[1],
-            edge = dx - xv[i0] > xv[i1] - dx ? i0 : i1;
-        var W = 0,
-            X = 0,
-            Y = 0,
-            XY = 0,
-            X2 = 0,
-            denom = 1 / Math.abs(xv[edge] - dx || 1); // Avoid singularity
-
-        for (var k = i0; k <= i1; ++k) {
-          var xk = xv[k],
-              yk = yv[k],
-              w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k],
-              xkw = xk * w;
-          W += w;
-          X += xkw;
-          Y += yk * w;
-          XY += yk * xkw;
-          X2 += xk * xkw;
-        } // Linear regression fit
-
-
-        var _ols = ols(X / W, Y / W, XY / W, X2 / W),
-            _ols2 = _slicedToArray(_ols, 2),
-            a = _ols2[0],
-            b = _ols2[1];
-
-        yhat[i] = a + b * dx;
-        residuals[i] = Math.abs(yv[i] - yhat[i]);
-        updateInterval(xv, i + 1, interval);
-      }
-
-      if (iter === maxiters) {
-        break;
-      }
-
-      var medianResidual = median(residuals);
-      if (Math.abs(medianResidual) < epsilon) break;
-
-      for (var _i = 0, arg, _w; _i < n; ++_i) {
-        arg = residuals[_i] / (6 * medianResidual); // Default to epsilon (rather than zero) for large deviations
-        // Keeping weights tiny but non-zero prevents singularites
-
-        robustWeights[_i] = arg >= 1 ? epsilon : (_w = 1 - arg * arg) * _w;
-      }
-    }
-
-    return output(xv, yhat, ux, uy);
-  }
-
-  loess.bandwidth = function (bw) {
-    return arguments.length ? (bandwidth = bw, loess) : bandwidth;
-  };
-
-  loess.x = function (fn) {
-    return arguments.length ? (x = fn, loess) : x;
-  };
-
-  loess.y = function (fn) {
-    return arguments.length ? (y = fn, loess) : y;
-  };
-
-  return loess;
-} // Weighting kernel for local regression
-
+// Adapted from science.js by Jason Davies
+var maxiters = 2, epsilon = 1e-12;
+function loess() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, bandwidth = 0.3;
+    var loessRegression = function loessRegression(data) {
+        var _a = points(data, function (dd) { return x(dd); }, function (dd) { return y(dd); }, true), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+        var n = xv.length;
+        var bw = Math.max(2, ~~(bandwidth * n)); // # of nearest neighbors
+        var yhat = new Float64Array(n);
+        var residuals = new Float64Array(n);
+        var robustWeights = new Float64Array(n).fill(1);
+        for (var iter = -1; ++iter <= maxiters;) {
+            var interval = [0, bw - 1];
+            for (var i = 0; i < n; ++i) {
+                var dx = xv[i];
+                var i0 = interval[0];
+                var i1 = interval[1];
+                var edge = (dx - xv[i0]) > (xv[i1] - dx) ? i0 : i1;
+                var W = 0, X = 0, Y = 0, XY = 0, X2 = 0;
+                var denom = 1 / Math.abs(xv[edge] - dx || 1);
+                for (var k = i0; k <= i1; ++k) {
+                    var xk = xv[k];
+                    var yk = yv[k];
+                    var w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k];
+                    var xkw = xk * w;
+                    W += w;
+                    X += xkw;
+                    Y += yk * w;
+                    XY += yk * xkw;
+                    X2 += xk * xkw;
+                }
+                // Linear regression fit
+                var _b = ols(X / W, Y / W, XY / W, X2 / W), a = _b[0], b = _b[1];
+                yhat[i] = a + b * dx;
+                residuals[i] = Math.abs(yv[i] - yhat[i]);
+                updateInterval(xv, i + 1, interval);
+            }
+            if (iter === maxiters) {
+                break;
+            }
+            var medianResidual = median(residuals);
+            if (Math.abs(medianResidual) < epsilon)
+                break;
+            for (var i = 0, arg = void 0, w = void 0; i < n; ++i) {
+                arg = residuals[i] / (6 * medianResidual);
+                // Default to epsilon (rather than zero) for large deviations
+                // Keeping weights tiny but non-zero prevents singularites
+                robustWeights[i] = (arg >= 1) ? epsilon : ((w = 1 - arg * arg) * w);
+            }
+        }
+        return output(xv, yhat, ux, uy);
+    };
+    loessRegression.bandwidth = function (bw) {
+        if (!arguments.length)
+            return bandwidth;
+        bandwidth = bw;
+        return loessRegression;
+    };
+    loessRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return loessRegression;
+    };
+    loessRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return loessRegression;
+    };
+    return loessRegression;
+}
+// Weighting kernel for local regression
 function tricube(x) {
-  return (x = 1 - x * x * x) * x * x;
-} // Advance sliding window interval of nearest neighbors
-
-
+    return (x = 1 - x * x * x) * x * x;
+}
+// Advance sliding window interval of nearest neighbors
 function updateInterval(xv, i, interval) {
-  var val = xv[i],
-      left = interval[0],
-      right = interval[1] + 1;
-  if (right >= xv.length) return; // Step right if distance to new right edge is <= distance to old left edge
-  // Step when distance is equal to ensure movement over duplicate x values
-
-  while (i > left && xv[right] - val <= val - xv[left]) {
-    interval[0] = ++left;
-    interval[1] = right;
-    ++right;
-  }
-} // Generate smoothed output points
+    var val = xv[i], left = interval[0], right = interval[1] + 1;
+    if (right >= xv.length)
+        return;
+    // Step right if distance to new right edge is <= distance to old left edge
+    // Step when distance is equal to ensure movement over duplicate x values
+    while (i > left && (xv[right] - val) <= (val - xv[left])) {
+        interval[0] = ++left;
+        interval[1] = right;
+        ++right;
+    }
+}
+// Generate smoothed output points
 // Average points with repeated x values
-
-
 function output(xv, yhat, ux, uy) {
-  var n = xv.length,
-      out = [];
-  var i = 0,
-      cnt = 0,
-      prev = [],
-      v;
-
-  for (; i < n; ++i) {
-    v = xv[i] + ux;
-
-    if (prev[0] === v) {
-      // Average output values via online update
-      prev[1] += (yhat[i] - prev[1]) / ++cnt;
-    } else {
-      // Add new output point
-      cnt = 0;
-      prev[1] += uy;
-      prev = [v, yhat[i]];
-      out.push(prev);
+    var n = xv.length, out = [];
+    var i = 0, cnt = 0, prev = [], v;
+    for (; i < n; ++i) {
+        v = xv[i] + ux;
+        if (prev[0] === v) {
+            // Average output values via online update
+            prev[1] += (yhat[i] - prev[1]) / (++cnt);
+        }
+        else {
+            // Add new output point
+            cnt = 0;
+            prev[1] += uy;
+            prev = [v, yhat[i]];
+            out.push(prev);
+        }
     }
-  }
-
-  prev[1] += uy;
-  return out;
-}
-
-function logarithmic () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      base = Math.E,
-      domain;
-
-  function logarithmic(data) {
-    var n = 0,
-        X = 0,
-        Y = 0,
-        XY = 0,
-        X2 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity,
-        lb = Math.log(base);
-    visitPoints(data, x, y, function (dx, dy) {
-      var lx = Math.log(dx) / lb;
-      ++n;
-      X += (lx - X) / n;
-      Y += (dy - Y) / n;
-      XY += (lx * dy - XY) / n;
-      X2 += (lx * lx - X2) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(X, Y, XY, X2),
-        _ols2 = _slicedToArray(_ols, 2),
-        intercept = _ols2[0],
-        slope = _ols2[1],
-        fn = function fn(x) {
-      return slope * Math.log(x) / lb + intercept;
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.a = slope;
-    out.b = intercept;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
+    prev[1] += uy;
     return out;
-  }
-
-  logarithmic.domain = function (arr) {
-    return arguments.length ? (domain = arr, logarithmic) : domain;
-  };
-
-  logarithmic.x = function (fn) {
-    return arguments.length ? (x = fn, logarithmic) : x;
-  };
-
-  logarithmic.y = function (fn) {
-    return arguments.length ? (y = fn, logarithmic) : y;
-  };
-
-  logarithmic.base = function (n) {
-    return arguments.length ? (base = n, logarithmic) : base;
-  };
-
-  return logarithmic;
 }
 
-function quad () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function quadratic(data) {
-    var _points = points(data, x, y),
-        _points2 = _slicedToArray(_points, 4),
-        xv = _points2[0],
-        yv = _points2[1],
-        ux = _points2[2],
-        uy = _points2[3],
-        n = xv.length;
-
-    var X2 = 0,
-        X3 = 0,
-        X4 = 0,
-        XY = 0,
-        X2Y = 0,
-        i,
-        dx,
-        dy,
-        x2;
-
-    for (i = 0; i < n;) {
-      dx = xv[i];
-      dy = yv[i++];
-      x2 = dx * dx;
-      X2 += (x2 - X2) / i;
-      X3 += (x2 * dx - X3) / i;
-      X4 += (x2 * x2 - X4) / i;
-      XY += (dx * dy - XY) / i;
-      X2Y += (x2 * dy - X2Y) / i;
-    }
-
-    var Y = 0,
-        n0 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      n0++;
-      Y += (dy - Y) / n0;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var X2X2 = X4 - X2 * X2,
-        d = X2 * X2X2 - X3 * X3,
-        a = (X2Y * X2 - XY * X3) / d,
-        b = (XY * X2X2 - X2Y * X3) / d,
-        c = -a * X2,
-        fn = function fn(x) {
-      x = x - ux;
-      return a * x * x + b * x + c + uy;
+function logarithmic() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, base = Math.E, domain;
+    var logarithmicRegression = function (data) {
+        var n = 0, X = 0, Y = 0, XY = 0, X2 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity, lb = Math.log(base);
+        visitPoints(data, x, y, function (dx, dy) {
+            var lx = Math.log(dx) / lb;
+            ++n;
+            X += (lx - X) / n;
+            Y += (dy - Y) / n;
+            XY += (lx * dy - XY) / n;
+            X2 += (lx * lx - X2) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(X, Y, XY, X2), intercept = _a[0], slope = _a[1];
+        var fn = function (xx) { return slope * Math.log(xx) / lb + intercept; };
+        var out = interpose(xmin, xmax, fn);
+        out.a = slope;
+        out.b = intercept;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
     };
-
-    var out = interpose(xmin, xmax, fn);
-    out.a = a;
-    out.b = b - 2 * a * ux;
-    out.c = c - b * ux + a * ux * ux + uy;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  quadratic.domain = function (arr) {
-    return arguments.length ? (domain = arr, quadratic) : domain;
-  };
-
-  quadratic.x = function (fn) {
-    return arguments.length ? (x = fn, quadratic) : x;
-  };
-
-  quadratic.y = function (fn) {
-    return arguments.length ? (y = fn, quadratic) : y;
-  };
-
-  return quadratic;
+    logarithmicRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return logarithmicRegression;
+    };
+    logarithmicRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return logarithmicRegression;
+    };
+    logarithmicRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return logarithmicRegression;
+    };
+    logarithmicRegression.base = function (b) {
+        if (!arguments.length)
+            return base;
+        base = b;
+        return logarithmicRegression;
+    };
+    return logarithmicRegression;
 }
 
-// Source: https://github.com/Tom-Alexander/regression-js/blob/master/src/regression.js#L246
-// License: https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE
-// ...with ideas from vega-statistics by Jeffrey Heer
-// Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/poly.js
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-
-function polynomial () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      order = 3,
-      domain;
-
-  function polynomial(data) {
-    // Use more efficient methods for lower orders
-    if (order === 1) {
-      var o = linear().x(x).y(y).domain(domain)(data);
-      o.coefficients = [o.b, o.a];
-      delete o.a;
-      delete o.b;
-      return o;
-    }
-
-    if (order === 2) {
-      var _o = quad().x(x).y(y).domain(domain)(data);
-
-      _o.coefficients = [_o.c, _o.b, _o.a];
-      delete _o.a;
-      delete _o.b;
-      delete _o.c;
-      return _o;
-    }
-
-    var _points = points(data, x, y),
-        _points2 = _slicedToArray(_points, 4),
-        xv = _points2[0],
-        yv = _points2[1],
-        ux = _points2[2],
-        uy = _points2[3],
-        n = xv.length,
-        lhs = [],
-        rhs = [],
-        k = order + 1;
-
-    var Y = 0,
-        n0 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      ++n0;
-      Y += (dy - Y) / n0;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-    var i, j, l, v, c;
-
-    for (i = 0; i < k; ++i) {
-      for (l = 0, v = 0; l < n; ++l) {
-        v += Math.pow(xv[l], i) * yv[l];
-      }
-
-      lhs.push(v);
-      c = new Float64Array(k);
-
-      for (j = 0; j < k; ++j) {
-        for (l = 0, v = 0; l < n; ++l) {
-          v += Math.pow(xv[l], i + j);
+function quadratic() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+    var quadraticRegression = function quadraticRegression(data) {
+        var _a = points(data, function (dd) { return x(dd); }, function (dd) { return y(dd); }), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+        var n = xv.length;
+        var X2 = 0, X3 = 0, X4 = 0, XY = 0, X2Y = 0, i, dx, dy, x2;
+        for (i = 0; i < n;) {
+            dx = xv[i];
+            dy = yv[i++];
+            x2 = dx * dx;
+            X2 += (x2 - X2) / i;
+            X3 += ((x2 * dx) - X3) / i;
+            X4 += ((x2 * x2) - X4) / i;
+            XY += ((dx * dy) - XY) / i;
+            X2Y += ((x2 * dy) - X2Y) / i;
         }
-
-        c[j] = v;
-      }
-
-      rhs.push(c);
-    }
-
-    rhs.push(lhs);
-
-    var coef = gaussianElimination(rhs),
-        fn = function fn(x) {
-      x -= ux;
-      var y = uy + coef[0] + coef[1] * x + coef[2] * x * x;
-
-      for (i = 3; i < k; ++i) {
-        y += coef[i] * Math.pow(x, i);
-      }
-
-      return y;
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.coefficients = uncenter(k, coef, -ux, uy);
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  polynomial.domain = function (arr) {
-    return arguments.length ? (domain = arr, polynomial) : domain;
-  };
-
-  polynomial.x = function (fn) {
-    return arguments.length ? (x = fn, polynomial) : x;
-  };
-
-  polynomial.y = function (fn) {
-    return arguments.length ? (y = fn, polynomial) : y;
-  };
-
-  polynomial.order = function (n) {
-    return arguments.length ? (order = n, polynomial) : order;
-  };
-
-  return polynomial;
+        var Y = 0, n0 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx2, dy2) {
+            n0++;
+            Y += (dy2 - Y) / n0;
+            if (!domain) {
+                if (dx2 < xmin)
+                    xmin = dx2;
+                if (dx2 > xmax)
+                    xmax = dx2;
+            }
+        });
+        var X2X2 = X4 - (X2 * X2);
+        var d = (X2 * X2X2 - X3 * X3);
+        var a = (X2Y * X2 - XY * X3) / d;
+        var b = (XY * X2X2 - X2Y * X3) / d;
+        var c = -a * X2;
+        var fn = function (xx) {
+            var shifted = xx - ux;
+            return a * shifted * shifted + b * shifted + c + uy;
+        };
+        var out = interpose(xmin, xmax, fn);
+        out.a = a;
+        out.b = b - 2 * a * ux;
+        out.c = c - b * ux + a * ux * ux + uy;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    quadraticRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return quadraticRegression;
+    };
+    quadraticRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return quadraticRegression;
+    };
+    quadraticRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return quadraticRegression;
+    };
+    return quadraticRegression;
 }
 
+// Adapted from regression-js by Tom Alexander
+function polynomial() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, order = 3, domain;
+    var polynomialRegression = function polynomialRegression(data) {
+        // Shortcut for lower-order polynomials:
+        if (order === 1) {
+            var o = linear().x(x).y(y).domain(domain)(data);
+            var result = [o[0], o[1]];
+            result.coefficients = [o.b, o.a];
+            result.predict = o.predict;
+            result.rSquared = o.rSquared;
+            return result;
+        }
+        if (order === 2) {
+            var o = quadratic().x(x).y(y).domain(domain)(data);
+            var result = [o[0], o[1]];
+            result.coefficients = [o.c, o.b, o.a];
+            result.predict = o.predict;
+            result.rSquared = o.rSquared;
+            return result;
+        }
+        var _a = points(data, x, y), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+        var n = xv.length;
+        var k = order + 1;
+        var lhs = [];
+        var rhs = [];
+        var Y = 0, n0 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            n0++;
+            Y += (dy - Y) / n0;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        // Build normal equations
+        for (var i = 0; i < k; i++) {
+            // LHS
+            var v = 0;
+            for (var l = 0; l < n; l++) {
+                v += Math.pow(xv[l], i) * yv[l];
+            }
+            lhs.push(v);
+            // RHS
+            var c = new Float64Array(k);
+            for (var j = 0; j < k; j++) {
+                var v2 = 0;
+                for (var l = 0; l < n; l++) {
+                    v2 += Math.pow(xv[l], i + j);
+                }
+                c[j] = v2;
+            }
+            rhs.push(c);
+        }
+        rhs.push(new Float64Array(lhs));
+        var coef = gaussianElimination(rhs);
+        var fn = function (xx) {
+            var shifted = xx - ux;
+            var val = uy + coef[0];
+            for (var i = 1; i < k; i++) {
+                val += coef[i] * Math.pow(shifted, i);
+            }
+            return val;
+        };
+        var out = interpose(xmin, xmax, fn);
+        out.coefficients = uncenter(k, coef, -ux, uy);
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    polynomialRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return polynomialRegression;
+    };
+    polynomialRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return polynomialRegression;
+    };
+    polynomialRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return polynomialRegression;
+    };
+    polynomialRegression.order = function (n) {
+        if (!arguments.length)
+            return order;
+        order = n;
+        return polynomialRegression;
+    };
+    return polynomialRegression;
+}
 function uncenter(k, a, x, y) {
-  var z = Array(k);
-  var i, j, v, c; // initialize to zero
-
-  for (i = 0; i < k; ++i) {
-    z[i] = 0;
-  } // polynomial expansion
-
-
-  for (i = k - 1; i >= 0; --i) {
-    v = a[i];
-    c = 1;
-    z[i] += v;
-
-    for (j = 1; j <= i; ++j) {
-      c *= (i + 1 - j) / j; // binomial coefficent
-
-      z[i - j] += v * Math.pow(x, j) * c;
+    var z = new Array(k).fill(0);
+    for (var i = k - 1; i >= 0; --i) {
+        var v = a[i];
+        z[i] += v;
+        var c = 1;
+        for (var j = 1; j <= i; ++j) {
+            c *= (i + 1 - j) / j; // binomial coefficient
+            z[i - j] += v * Math.pow(x, j) * c;
+        }
     }
-  } // bias term
-
-
-  z[0] += y;
-  return z;
-} // Given an array for a two-dimensional matrix and the polynomial order,
-// solve A * x = b using Gaussian elimination.
-
-
+    // bias term
+    z[0] += y;
+    return z;
+}
+// Solve A * x = b using Gaussian elimination
 function gaussianElimination(matrix) {
-  var n = matrix.length - 1,
-      coef = [];
-  var i, j, k, r, t;
-
-  for (i = 0; i < n; ++i) {
-    r = i; // max row
-
-    for (j = i + 1; j < n; ++j) {
-      if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
-        r = j;
-      }
-    }
-
-    for (k = i; k < n + 1; ++k) {
-      t = matrix[k][i];
-      matrix[k][i] = matrix[k][r];
-      matrix[k][r] = t;
-    }
-
-    for (j = i + 1; j < n; ++j) {
-      for (k = n; k >= i; k--) {
-        matrix[k][j] -= matrix[k][i] * matrix[i][j] / matrix[i][i];
-      }
+    var n = matrix.length - 1;
+    var coef = new Array(n);
+    for (var i = 0; i < n; i++) {
+        var r = i;
+        // find pivot row
+        for (var j = i + 1; j < n; j++) {
+            if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
+                r = j;
+            }
+        }
+        // swap columns
+        for (var k = i; k < n + 1; k++) {
+            var t = matrix[k][i];
+            matrix[k][i] = matrix[k][r];
+            matrix[k][r] = t;
+        }
+        // reduce
+        for (var j = i + 1; j < n; j++) {
+            for (var k = n; k >= i; k--) {
+                matrix[k][j] -= (matrix[k][i] * matrix[i][j]) / matrix[i][i];
+            }
+        }
     }
-  }
-
-  for (j = n - 1; j >= 0; --j) {
-    t = 0;
-
-    for (k = j + 1; k < n; ++k) {
-      t += matrix[k][j] * coef[k];
+    for (var j = n - 1; j >= 0; j--) {
+        var t = 0;
+        for (var k = j + 1; k < n; k++) {
+            t += matrix[k][j] * coef[k];
+        }
+        coef[j] = (matrix[n][j] - t) / matrix[j][j];
     }
-
-    coef[j] = (matrix[n][j] - t) / matrix[j][j];
-  }
-
-  return coef;
+    return coef;
 }
 
-function power () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function power(data) {
-    var n = 0,
-        X = 0,
-        Y = 0,
-        XY = 0,
-        X2 = 0,
-        YS = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      var lx = Math.log(dx),
-          ly = Math.log(dy);
-      ++n;
-      X += (lx - X) / n;
-      Y += (ly - Y) / n;
-      XY += (lx * ly - XY) / n;
-      X2 += (lx * lx - X2) / n;
-      YS += (dy - YS) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(X, Y, XY, X2),
-        _ols2 = _slicedToArray(_ols, 2),
-        a = _ols2[0],
-        b = _ols2[1];
-
-    a = Math.exp(a);
-
-    var fn = function fn(x) {
-      return a * Math.pow(x, b);
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.a = a;
-    out.b = b;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, YS, fn);
-    return out;
-  }
-
-  power.domain = function (arr) {
-    return arguments.length ? (domain = arr, power) : domain;
-  };
-
-  power.x = function (fn) {
-    return arguments.length ? (x = fn, power) : x;
-  };
-
-  power.y = function (fn) {
-    return arguments.length ? (y = fn, power) : y;
-  };
+function power() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+    var powerRegression = function powerRegression(data) {
+        var n = 0, X = 0, Y = 0, XY = 0, X2 = 0, YS = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            var lx = Math.log(dx), ly = Math.log(dy);
+            ++n;
+            X += (lx - X) / n;
+            Y += (ly - Y) / n;
+            XY += (lx * ly - XY) / n;
+            X2 += (lx * lx - X2) / n;
+            YS += (dy - YS) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(X, Y, XY, X2), a = _a[0], b = _a[1];
+        a = Math.exp(a);
+        var fn = function (xx) { return a * Math.pow(xx, b); };
+        var out = interpose(xmin, xmax, fn);
+        out.a = a;
+        out.b = b;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, YS, fn);
+        return out;
+    };
+    powerRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return powerRegression;
+    };
+    powerRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return powerRegression;
+    };
+    powerRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return powerRegression;
+    };
+    return powerRegression;
+}
 
-  return power;
+function sigmoidal() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain, maxIter = 2000, alpha = 1e-4; // learning rate
+    function sigmoidalRegression(data) {
+        var n = 0, Xmin = domain ? +domain[0] : Infinity, Xmax = domain ? +domain[1] : -Infinity, Ymin = Infinity, Ymax = -Infinity, sumY = 0;
+        // Gather data & track min/max for domain if not preset
+        visitPoints(data, x, y, function (dx, dy) {
+            n++;
+            sumY += dy;
+            if (!domain) {
+                if (dx < Xmin)
+                    Xmin = dx;
+                if (dx > Xmax)
+                    Xmax = dx;
+            }
+            if (dy < Ymin)
+                Ymin = dy;
+            if (dy > Ymax)
+                Ymax = dy;
+        });
+        if (!domain && (Xmin === Infinity || Xmax === -Infinity)) {
+            Xmin = 0;
+            Xmax = 1;
+        }
+        // Initialize parameters
+        var A = Ymax - Ymin; // amplitude
+        var B = 1; // logistic slope
+        var C = Ymin; // baseline offset
+        var M = (Xmin + Xmax) / 2; // midpoint
+        // Predict function
+        function f(xx) {
+            return C + A / (1 + Math.exp(-B * (xx - M)));
+        }
+        var _loop_1 = function (iter) {
+            var dA = 0, dB = 0, dC = 0, dM = 0;
+            visitPoints(data, x, y, function (dx, dy) {
+                var yhat = f(dx);
+                var err = dy - yhat;
+                var ex = Math.exp(-B * (dx - M));
+                var g = 1 / (1 + ex); // logistic
+                // partial derivatives
+                var df_dA = g;
+                var df_dC = 1;
+                var df_dB = A * g * (1 - g) * (dx - M);
+                // Note the negative sign for M:
+                var df_dM = -A * B * g * (1 - g);
+                var factor = -2 * err;
+                dA += factor * df_dA;
+                dB += factor * df_dB;
+                dC += factor * df_dC;
+                dM += factor * df_dM;
+            });
+            A -= alpha * (dA / n);
+            B -= alpha * (dB / n);
+            C -= alpha * (dC / n);
+            M -= alpha * (dM / n);
+        };
+        // Gradient Descent
+        for (var iter = 0; iter < maxIter; iter++) {
+            _loop_1();
+        }
+        var predict = function (xx) { return C + A / (1 + Math.exp(-B * (xx - M))); };
+        var out = interpose(Xmin, Xmax, predict);
+        out.A = A;
+        out.B = B;
+        out.C = C;
+        out.M = M;
+        out.predict = predict;
+        // R^2
+        var meanY = sumY / n;
+        out.rSquared = determination(data, x, y, meanY, predict);
+        return out;
+    }
+    sigmoidalRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return sigmoidalRegression;
+    };
+    sigmoidalRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return sigmoidalRegression;
+    };
+    sigmoidalRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return sigmoidalRegression;
+    };
+    return sigmoidalRegression;
 }
 
 exports.regressionExp = exponential;
@@ -876,4 +767,5 @@ exports.regressionLoess = loess;
 exports.regressionLog = logarithmic;
 exports.regressionPoly = polynomial;
 exports.regressionPow = power;
-exports.regressionQuad = quad;
+exports.regressionQuad = quadratic;
+exports.regressionSigmoidal = sigmoidal;
diff --git a/dist/d3-regression.esm.js b/dist/d3-regression.esm.js
index c1a206b..f4d98e6 100644
--- a/dist/d3-regression.esm.js
+++ b/dist/d3-regression.esm.js
@@ -1,869 +1,760 @@
-// https://github.com/HarryStevens/d3-regression#readme Version 1.3.10. Copyright 2022 Harry Stevens.
-function _slicedToArray(arr, i) {
-  return _arrayWithHoles(arr) || _iterableToArrayLimit(arr, i) || _nonIterableRest();
-}
-
-function _arrayWithHoles(arr) {
-  if (Array.isArray(arr)) return arr;
-}
-
-function _iterableToArrayLimit(arr, i) {
-  var _arr = [];
-  var _n = true;
-  var _d = false;
-  var _e = undefined;
-
-  try {
-    for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) {
-      _arr.push(_s.value);
-
-      if (i && _arr.length === i) break;
-    }
-  } catch (err) {
-    _d = true;
-    _e = err;
-  } finally {
-    try {
-      if (!_n && _i["return"] != null) _i["return"]();
-    } finally {
-      if (_d) throw _e;
-    }
-  }
-
-  return _arr;
-}
-
-function _nonIterableRest() {
-  throw new TypeError("Invalid attempt to destructure non-iterable instance");
-}
-
-// Adapted from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+// https://github.com/HarryStevens/d3-regression#readme Version 1.3.10. Copyright 2025 Harry Stevens.
+/**
+ * Adapted from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+ */
 function points(data, x, y, sort) {
-  data = data.filter(function (d, i) {
-    var u = x(d, i),
-        v = y(d, i);
-    return u != null && isFinite(u) && v != null && isFinite(v);
-  });
-
-  if (sort) {
-    data.sort(function (a, b) {
-      return x(a) - x(b);
+    data = data.filter(function (d, i) {
+        var u = x(d, i), v = y(d, i);
+        return u != null && isFinite(u) && v != null && isFinite(v);
     });
-  }
-
-  var n = data.length,
-      X = new Float64Array(n),
-      Y = new Float64Array(n); // extract values, calculate means
-
-  var ux = 0,
-      uy = 0,
-      xv,
-      yv,
-      d;
-
-  for (var i = 0; i < n;) {
-    d = data[i];
-    X[i] = xv = +x(d, i, data);
-    Y[i] = yv = +y(d, i, data);
-    ++i;
-    ux += (xv - ux) / i;
-    uy += (yv - uy) / i;
-  } // mean center the data
-
-
-  for (var _i = 0; _i < n; ++_i) {
-    X[_i] -= ux;
-    Y[_i] -= uy;
-  }
-
-  return [X, Y, ux, uy];
+    if (sort) {
+        data.sort(function (a, b) { return x(a) - x(b); });
+    }
+    var n = data.length, X = new Float64Array(n), Y = new Float64Array(n);
+    // extract values, calculate means
+    var ux = 0, uy = 0, xv, yv, d;
+    for (var i = 0; i < n;) {
+        d = data[i];
+        X[i] = xv = +x(d, i, data);
+        Y[i] = yv = +y(d, i, data);
+        ++i;
+        ux += (xv - ux) / i;
+        uy += (yv - uy) / i;
+    }
+    // mean center the data
+    for (var i = 0; i < n; ++i) {
+        X[i] -= ux;
+        Y[i] -= uy;
+    }
+    return [X, Y, ux, uy];
 }
+/**
+ * Iterates over valid data points, invoking a callback for each.
+ */
 function visitPoints(data, x, y, cb) {
-  var iterations = 0;
-
-  for (var i = 0, n = data.length; i < n; i++) {
-    var d = data[i],
-        dx = +x(d, i, data),
-        dy = +y(d, i, data);
-
-    if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
-      cb(dx, dy, iterations++);
+    var iterations = 0;
+    for (var i = 0; i < data.length; i++) {
+        var d = data[i];
+        var dx = +x(d, i, data);
+        var dy = +y(d, i, data);
+        if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
+            cb(dx, dy, iterations++);
+        }
     }
-  }
 }
 
-// return the coefficient of determination, or R squared.
-
+/**
+ * Given a dataset, x- and y-accessors, the mean center of the y-values (uY),
+ * and a prediction function, return the coefficient of determination, R^2.
+ */
 function determination(data, x, y, uY, predict) {
-  var SSE = 0,
-      SST = 0;
-  visitPoints(data, x, y, function (dx, dy) {
-    var sse = dy - predict(dx),
-        sst = dy - uY;
-    SSE += sse * sse;
-    SST += sst * sst;
-  });
-  return 1 - SSE / SST;
+    var SSE = 0, // Sum of Squared Errors
+    SST = 0; // Total Sum of Squares
+    visitPoints(data, x, y, function (dx, dy) {
+        var sse = dy - predict(dx);
+        var sst = dy - uY;
+        SSE += sse * sse;
+        SST += sst * sst;
+    });
+    return 1 - SSE / SST;
 }
 
-// Returns the angle of a line in degrees.
+/**
+ * Returns the angle of a line in degrees.
+ */
 function angle(line) {
-  return Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) * 180 / Math.PI;
-} // Returns the midpoint of a line.
-
+    return (Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) *
+        (180 / Math.PI));
+}
+/**
+ * Returns the midpoint of a line.
+ */
 function midpoint(line) {
-  return [(line[0][0] + line[1][0]) / 2, (line[0][1] + line[1][1]) / 2];
+    return [
+        (line[0][0] + line[1][0]) / 2,
+        (line[0][1] + line[1][1]) / 2
+    ];
 }
 
-// returns a smooth line.
-
+/**
+ * Given a start point (xmin), an end point (xmax),
+ * and a prediction function, returns a smooth line.
+ */
 function interpose(xmin, xmax, predict) {
-  var l = Math.log(xmax - xmin) * Math.LOG10E + 1 | 0;
-  var precision = 1 * Math.pow(10, -l / 2 - 1),
-      maxIter = 1e4;
-  var points = [px(xmin), px(xmax)],
-      iter = 0;
-
-  while (find(points) && iter < maxIter) {
-  }
-
-  return points;
-
-  function px(x) {
-    return [x, predict(x)];
-  }
-
-  function find(points) {
-    iter++;
-    var n = points.length;
-    var found = false;
-
-    for (var i = 0; i < n - 1; i++) {
-      var p0 = points[i],
-          p1 = points[i + 1],
-          m = midpoint([p0, p1]),
-          mp = px(m[0]),
-          a0 = angle([p0, m]),
-          a1 = angle([p0, mp]),
-          a = Math.abs(a0 - a1);
-
-      if (a > precision) {
-        points.splice(i + 1, 0, mp);
-        found = true;
-      }
+    var l = (Math.log(xmax - xmin) * Math.LOG10E + 1) | 0;
+    var precision = Math.pow(10, -l / 2 - 1);
+    var maxIter = 1e4;
+    var points = [px(xmin), px(xmax)];
+    var iter = 0;
+    while (find(points) && iter < maxIter)
+        ;
+    return points;
+    function px(x) {
+        return [x, predict(x)];
+    }
+    function find(points) {
+        iter++;
+        var n = points.length;
+        var found = false;
+        for (var i = 0; i < n - 1; i++) {
+            var p0 = points[i];
+            var p1 = points[i + 1];
+            var m = midpoint([p0, p1]);
+            var mp = px(m[0]);
+            var a0 = angle([p0, m]);
+            var a1 = angle([p0, mp]);
+            var a = Math.abs(a0 - a1);
+            if (a > precision) {
+                points.splice(i + 1, 0, mp);
+                found = true;
+            }
+        }
+        return found;
     }
-
-    return found;
-  }
 }
 
-// Ordinary Least Squares from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+/**
+ * Ordinary Least Squares from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+ */
 function ols(uX, uY, uXY, uX2) {
-  var delta = uX2 - uX * uX,
-      slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta,
-      intercept = uY - slope * uX;
-  return [intercept, slope];
+    var delta = uX2 - uX * uX, slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta, intercept = uY - slope * uX;
+    return [intercept, slope];
 }
 
-function exponential () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function exponential(data) {
-    var n = 0,
-        Y = 0,
-        YL = 0,
-        XY = 0,
-        XYL = 0,
-        X2Y = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      var ly = Math.log(dy),
-          xy = dx * dy;
-      ++n;
-      Y += (dy - Y) / n;
-      XY += (xy - XY) / n;
-      X2Y += (dx * xy - X2Y) / n;
-      YL += (dy * ly - YL) / n;
-      XYL += (xy * ly - XYL) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y),
-        _ols2 = _slicedToArray(_ols, 2),
-        a = _ols2[0],
-        b = _ols2[1];
-
-    a = Math.exp(a);
-
-    var fn = function fn(x) {
-      return a * Math.exp(b * x);
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.a = a;
-    out.b = b;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  exponential.domain = function (arr) {
-    return arguments.length ? (domain = arr, exponential) : domain;
-  };
-
-  exponential.x = function (fn) {
-    return arguments.length ? (x = fn, exponential) : x;
-  };
-
-  exponential.y = function (fn) {
-    return arguments.length ? (y = fn, exponential) : y;
-  };
-
-  return exponential;
+function exponential() {
+    var y = function (d) { return d[1]; }, x = function (d) { return d[0]; }, domain;
+    var exponentialRegression = function (data) {
+        var n = 0, Y = 0, YL = 0, XY = 0, XYL = 0, X2Y = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            var ly = Math.log(dy), xy = dx * dy;
+            ++n;
+            Y += (dy - Y) / n;
+            XY += (xy - XY) / n;
+            X2Y += (dx * xy - X2Y) / n;
+            YL += (dy * ly - YL) / n;
+            XYL += (xy * ly - XYL) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y), a = _a[0], b = _a[1];
+        a = Math.exp(a);
+        var fn = function (xx) { return a * Math.exp(b * xx); };
+        var out = interpose(xmin, xmax, fn);
+        out.a = a;
+        out.b = b;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    exponentialRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return exponentialRegression;
+    };
+    exponentialRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return exponentialRegression;
+    };
+    exponentialRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return exponentialRegression;
+    };
+    return exponentialRegression;
 }
 
-function linear () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function linear(data) {
-    var n = 0,
-        X = 0,
-        // sum of x
-    Y = 0,
-        // sum of y
-    XY = 0,
-        // sum of x * y
-    X2 = 0,
-        // sum of x * x
-    xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      ++n;
-      X += (dx - X) / n;
-      Y += (dy - Y) / n;
-      XY += (dx * dy - XY) / n;
-      X2 += (dx * dx - X2) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(X, Y, XY, X2),
-        _ols2 = _slicedToArray(_ols, 2),
-        intercept = _ols2[0],
-        slope = _ols2[1],
-        fn = function fn(x) {
-      return slope * x + intercept;
-    },
-        out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
-
-    out.a = slope;
-    out.b = intercept;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  linear.domain = function (arr) {
-    return arguments.length ? (domain = arr, linear) : domain;
-  };
-
-  linear.x = function (fn) {
-    return arguments.length ? (x = fn, linear) : x;
-  };
-
-  linear.y = function (fn) {
-    return arguments.length ? (y = fn, linear) : y;
-  };
-
-  return linear;
+function linear() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+    var linearRegression = function (data) {
+        var n = 0, X = 0, // sum of x
+        Y = 0, // sum of y
+        XY = 0, // sum of x*y
+        X2 = 0, // sum of x*x
+        xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            ++n;
+            X += (dx - X) / n;
+            Y += (dy - Y) / n;
+            XY += (dx * dy - XY) / n;
+            X2 += (dx * dx - X2) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(X, Y, XY, X2), intercept = _a[0], slope = _a[1];
+        var fn = function (xx) { return slope * xx + intercept; };
+        var out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
+        out.a = slope;
+        out.b = intercept;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    linearRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return linearRegression;
+    };
+    linearRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return linearRegression;
+    };
+    linearRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return linearRegression;
+    };
+    return linearRegression;
 }
 
-// Returns the medium value of an array of numbers.
+/**
+ * Returns the median value of an array of numbers.
+ */
 function median(arr) {
-  arr.sort(function (a, b) {
-    return a - b;
-  });
-  var i = arr.length / 2;
-  return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
+    arr.sort(function (a, b) { return a - b; });
+    var i = arr.length / 2;
+    return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
 }
 
-var maxiters = 2,
-    epsilon = 1e-12;
-function loess () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      bandwidth = .3;
-
-  function loess(data) {
-    var _points = points(data, x, y, true),
-        _points2 = _slicedToArray(_points, 4),
-        xv = _points2[0],
-        yv = _points2[1],
-        ux = _points2[2],
-        uy = _points2[3],
-        n = xv.length,
-        bw = Math.max(2, ~~(bandwidth * n)),
-        yhat = new Float64Array(n),
-        residuals = new Float64Array(n),
-        robustWeights = new Float64Array(n).fill(1);
-
-    for (var iter = -1; ++iter <= maxiters;) {
-      var interval = [0, bw - 1];
-
-      for (var i = 0; i < n; ++i) {
-        var dx = xv[i],
-            i0 = interval[0],
-            i1 = interval[1],
-            edge = dx - xv[i0] > xv[i1] - dx ? i0 : i1;
-        var W = 0,
-            X = 0,
-            Y = 0,
-            XY = 0,
-            X2 = 0,
-            denom = 1 / Math.abs(xv[edge] - dx || 1); // Avoid singularity
-
-        for (var k = i0; k <= i1; ++k) {
-          var xk = xv[k],
-              yk = yv[k],
-              w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k],
-              xkw = xk * w;
-          W += w;
-          X += xkw;
-          Y += yk * w;
-          XY += yk * xkw;
-          X2 += xk * xkw;
-        } // Linear regression fit
-
-
-        var _ols = ols(X / W, Y / W, XY / W, X2 / W),
-            _ols2 = _slicedToArray(_ols, 2),
-            a = _ols2[0],
-            b = _ols2[1];
-
-        yhat[i] = a + b * dx;
-        residuals[i] = Math.abs(yv[i] - yhat[i]);
-        updateInterval(xv, i + 1, interval);
-      }
-
-      if (iter === maxiters) {
-        break;
-      }
-
-      var medianResidual = median(residuals);
-      if (Math.abs(medianResidual) < epsilon) break;
-
-      for (var _i = 0, arg, _w; _i < n; ++_i) {
-        arg = residuals[_i] / (6 * medianResidual); // Default to epsilon (rather than zero) for large deviations
-        // Keeping weights tiny but non-zero prevents singularites
-
-        robustWeights[_i] = arg >= 1 ? epsilon : (_w = 1 - arg * arg) * _w;
-      }
-    }
-
-    return output(xv, yhat, ux, uy);
-  }
-
-  loess.bandwidth = function (bw) {
-    return arguments.length ? (bandwidth = bw, loess) : bandwidth;
-  };
-
-  loess.x = function (fn) {
-    return arguments.length ? (x = fn, loess) : x;
-  };
-
-  loess.y = function (fn) {
-    return arguments.length ? (y = fn, loess) : y;
-  };
-
-  return loess;
-} // Weighting kernel for local regression
-
+// Adapted from science.js by Jason Davies
+var maxiters = 2, epsilon = 1e-12;
+function loess() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, bandwidth = 0.3;
+    var loessRegression = function loessRegression(data) {
+        var _a = points(data, function (dd) { return x(dd); }, function (dd) { return y(dd); }, true), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+        var n = xv.length;
+        var bw = Math.max(2, ~~(bandwidth * n)); // # of nearest neighbors
+        var yhat = new Float64Array(n);
+        var residuals = new Float64Array(n);
+        var robustWeights = new Float64Array(n).fill(1);
+        for (var iter = -1; ++iter <= maxiters;) {
+            var interval = [0, bw - 1];
+            for (var i = 0; i < n; ++i) {
+                var dx = xv[i];
+                var i0 = interval[0];
+                var i1 = interval[1];
+                var edge = (dx - xv[i0]) > (xv[i1] - dx) ? i0 : i1;
+                var W = 0, X = 0, Y = 0, XY = 0, X2 = 0;
+                var denom = 1 / Math.abs(xv[edge] - dx || 1);
+                for (var k = i0; k <= i1; ++k) {
+                    var xk = xv[k];
+                    var yk = yv[k];
+                    var w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k];
+                    var xkw = xk * w;
+                    W += w;
+                    X += xkw;
+                    Y += yk * w;
+                    XY += yk * xkw;
+                    X2 += xk * xkw;
+                }
+                // Linear regression fit
+                var _b = ols(X / W, Y / W, XY / W, X2 / W), a = _b[0], b = _b[1];
+                yhat[i] = a + b * dx;
+                residuals[i] = Math.abs(yv[i] - yhat[i]);
+                updateInterval(xv, i + 1, interval);
+            }
+            if (iter === maxiters) {
+                break;
+            }
+            var medianResidual = median(residuals);
+            if (Math.abs(medianResidual) < epsilon)
+                break;
+            for (var i = 0, arg = void 0, w = void 0; i < n; ++i) {
+                arg = residuals[i] / (6 * medianResidual);
+                // Default to epsilon (rather than zero) for large deviations
+                // Keeping weights tiny but non-zero prevents singularites
+                robustWeights[i] = (arg >= 1) ? epsilon : ((w = 1 - arg * arg) * w);
+            }
+        }
+        return output(xv, yhat, ux, uy);
+    };
+    loessRegression.bandwidth = function (bw) {
+        if (!arguments.length)
+            return bandwidth;
+        bandwidth = bw;
+        return loessRegression;
+    };
+    loessRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return loessRegression;
+    };
+    loessRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return loessRegression;
+    };
+    return loessRegression;
+}
+// Weighting kernel for local regression
 function tricube(x) {
-  return (x = 1 - x * x * x) * x * x;
-} // Advance sliding window interval of nearest neighbors
-
-
+    return (x = 1 - x * x * x) * x * x;
+}
+// Advance sliding window interval of nearest neighbors
 function updateInterval(xv, i, interval) {
-  var val = xv[i],
-      left = interval[0],
-      right = interval[1] + 1;
-  if (right >= xv.length) return; // Step right if distance to new right edge is <= distance to old left edge
-  // Step when distance is equal to ensure movement over duplicate x values
-
-  while (i > left && xv[right] - val <= val - xv[left]) {
-    interval[0] = ++left;
-    interval[1] = right;
-    ++right;
-  }
-} // Generate smoothed output points
+    var val = xv[i], left = interval[0], right = interval[1] + 1;
+    if (right >= xv.length)
+        return;
+    // Step right if distance to new right edge is <= distance to old left edge
+    // Step when distance is equal to ensure movement over duplicate x values
+    while (i > left && (xv[right] - val) <= (val - xv[left])) {
+        interval[0] = ++left;
+        interval[1] = right;
+        ++right;
+    }
+}
+// Generate smoothed output points
 // Average points with repeated x values
-
-
 function output(xv, yhat, ux, uy) {
-  var n = xv.length,
-      out = [];
-  var i = 0,
-      cnt = 0,
-      prev = [],
-      v;
-
-  for (; i < n; ++i) {
-    v = xv[i] + ux;
-
-    if (prev[0] === v) {
-      // Average output values via online update
-      prev[1] += (yhat[i] - prev[1]) / ++cnt;
-    } else {
-      // Add new output point
-      cnt = 0;
-      prev[1] += uy;
-      prev = [v, yhat[i]];
-      out.push(prev);
+    var n = xv.length, out = [];
+    var i = 0, cnt = 0, prev = [], v;
+    for (; i < n; ++i) {
+        v = xv[i] + ux;
+        if (prev[0] === v) {
+            // Average output values via online update
+            prev[1] += (yhat[i] - prev[1]) / (++cnt);
+        }
+        else {
+            // Add new output point
+            cnt = 0;
+            prev[1] += uy;
+            prev = [v, yhat[i]];
+            out.push(prev);
+        }
     }
-  }
-
-  prev[1] += uy;
-  return out;
-}
-
-function logarithmic () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      base = Math.E,
-      domain;
-
-  function logarithmic(data) {
-    var n = 0,
-        X = 0,
-        Y = 0,
-        XY = 0,
-        X2 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity,
-        lb = Math.log(base);
-    visitPoints(data, x, y, function (dx, dy) {
-      var lx = Math.log(dx) / lb;
-      ++n;
-      X += (lx - X) / n;
-      Y += (dy - Y) / n;
-      XY += (lx * dy - XY) / n;
-      X2 += (lx * lx - X2) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(X, Y, XY, X2),
-        _ols2 = _slicedToArray(_ols, 2),
-        intercept = _ols2[0],
-        slope = _ols2[1],
-        fn = function fn(x) {
-      return slope * Math.log(x) / lb + intercept;
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.a = slope;
-    out.b = intercept;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
+    prev[1] += uy;
     return out;
-  }
-
-  logarithmic.domain = function (arr) {
-    return arguments.length ? (domain = arr, logarithmic) : domain;
-  };
-
-  logarithmic.x = function (fn) {
-    return arguments.length ? (x = fn, logarithmic) : x;
-  };
-
-  logarithmic.y = function (fn) {
-    return arguments.length ? (y = fn, logarithmic) : y;
-  };
-
-  logarithmic.base = function (n) {
-    return arguments.length ? (base = n, logarithmic) : base;
-  };
-
-  return logarithmic;
 }
 
-function quad () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function quadratic(data) {
-    var _points = points(data, x, y),
-        _points2 = _slicedToArray(_points, 4),
-        xv = _points2[0],
-        yv = _points2[1],
-        ux = _points2[2],
-        uy = _points2[3],
-        n = xv.length;
-
-    var X2 = 0,
-        X3 = 0,
-        X4 = 0,
-        XY = 0,
-        X2Y = 0,
-        i,
-        dx,
-        dy,
-        x2;
-
-    for (i = 0; i < n;) {
-      dx = xv[i];
-      dy = yv[i++];
-      x2 = dx * dx;
-      X2 += (x2 - X2) / i;
-      X3 += (x2 * dx - X3) / i;
-      X4 += (x2 * x2 - X4) / i;
-      XY += (dx * dy - XY) / i;
-      X2Y += (x2 * dy - X2Y) / i;
-    }
-
-    var Y = 0,
-        n0 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      n0++;
-      Y += (dy - Y) / n0;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var X2X2 = X4 - X2 * X2,
-        d = X2 * X2X2 - X3 * X3,
-        a = (X2Y * X2 - XY * X3) / d,
-        b = (XY * X2X2 - X2Y * X3) / d,
-        c = -a * X2,
-        fn = function fn(x) {
-      x = x - ux;
-      return a * x * x + b * x + c + uy;
+function logarithmic() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, base = Math.E, domain;
+    var logarithmicRegression = function (data) {
+        var n = 0, X = 0, Y = 0, XY = 0, X2 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity, lb = Math.log(base);
+        visitPoints(data, x, y, function (dx, dy) {
+            var lx = Math.log(dx) / lb;
+            ++n;
+            X += (lx - X) / n;
+            Y += (dy - Y) / n;
+            XY += (lx * dy - XY) / n;
+            X2 += (lx * lx - X2) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(X, Y, XY, X2), intercept = _a[0], slope = _a[1];
+        var fn = function (xx) { return slope * Math.log(xx) / lb + intercept; };
+        var out = interpose(xmin, xmax, fn);
+        out.a = slope;
+        out.b = intercept;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
     };
-
-    var out = interpose(xmin, xmax, fn);
-    out.a = a;
-    out.b = b - 2 * a * ux;
-    out.c = c - b * ux + a * ux * ux + uy;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  quadratic.domain = function (arr) {
-    return arguments.length ? (domain = arr, quadratic) : domain;
-  };
-
-  quadratic.x = function (fn) {
-    return arguments.length ? (x = fn, quadratic) : x;
-  };
-
-  quadratic.y = function (fn) {
-    return arguments.length ? (y = fn, quadratic) : y;
-  };
-
-  return quadratic;
+    logarithmicRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return logarithmicRegression;
+    };
+    logarithmicRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return logarithmicRegression;
+    };
+    logarithmicRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return logarithmicRegression;
+    };
+    logarithmicRegression.base = function (b) {
+        if (!arguments.length)
+            return base;
+        base = b;
+        return logarithmicRegression;
+    };
+    return logarithmicRegression;
 }
 
-// Source: https://github.com/Tom-Alexander/regression-js/blob/master/src/regression.js#L246
-// License: https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE
-// ...with ideas from vega-statistics by Jeffrey Heer
-// Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/poly.js
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-
-function polynomial () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      order = 3,
-      domain;
-
-  function polynomial(data) {
-    // Use more efficient methods for lower orders
-    if (order === 1) {
-      var o = linear().x(x).y(y).domain(domain)(data);
-      o.coefficients = [o.b, o.a];
-      delete o.a;
-      delete o.b;
-      return o;
-    }
-
-    if (order === 2) {
-      var _o = quad().x(x).y(y).domain(domain)(data);
-
-      _o.coefficients = [_o.c, _o.b, _o.a];
-      delete _o.a;
-      delete _o.b;
-      delete _o.c;
-      return _o;
-    }
-
-    var _points = points(data, x, y),
-        _points2 = _slicedToArray(_points, 4),
-        xv = _points2[0],
-        yv = _points2[1],
-        ux = _points2[2],
-        uy = _points2[3],
-        n = xv.length,
-        lhs = [],
-        rhs = [],
-        k = order + 1;
-
-    var Y = 0,
-        n0 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      ++n0;
-      Y += (dy - Y) / n0;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-    var i, j, l, v, c;
-
-    for (i = 0; i < k; ++i) {
-      for (l = 0, v = 0; l < n; ++l) {
-        v += Math.pow(xv[l], i) * yv[l];
-      }
-
-      lhs.push(v);
-      c = new Float64Array(k);
-
-      for (j = 0; j < k; ++j) {
-        for (l = 0, v = 0; l < n; ++l) {
-          v += Math.pow(xv[l], i + j);
+function quadratic() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+    var quadraticRegression = function quadraticRegression(data) {
+        var _a = points(data, function (dd) { return x(dd); }, function (dd) { return y(dd); }), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+        var n = xv.length;
+        var X2 = 0, X3 = 0, X4 = 0, XY = 0, X2Y = 0, i, dx, dy, x2;
+        for (i = 0; i < n;) {
+            dx = xv[i];
+            dy = yv[i++];
+            x2 = dx * dx;
+            X2 += (x2 - X2) / i;
+            X3 += ((x2 * dx) - X3) / i;
+            X4 += ((x2 * x2) - X4) / i;
+            XY += ((dx * dy) - XY) / i;
+            X2Y += ((x2 * dy) - X2Y) / i;
         }
-
-        c[j] = v;
-      }
-
-      rhs.push(c);
-    }
-
-    rhs.push(lhs);
-
-    var coef = gaussianElimination(rhs),
-        fn = function fn(x) {
-      x -= ux;
-      var y = uy + coef[0] + coef[1] * x + coef[2] * x * x;
-
-      for (i = 3; i < k; ++i) {
-        y += coef[i] * Math.pow(x, i);
-      }
-
-      return y;
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.coefficients = uncenter(k, coef, -ux, uy);
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    return out;
-  }
-
-  polynomial.domain = function (arr) {
-    return arguments.length ? (domain = arr, polynomial) : domain;
-  };
-
-  polynomial.x = function (fn) {
-    return arguments.length ? (x = fn, polynomial) : x;
-  };
-
-  polynomial.y = function (fn) {
-    return arguments.length ? (y = fn, polynomial) : y;
-  };
-
-  polynomial.order = function (n) {
-    return arguments.length ? (order = n, polynomial) : order;
-  };
-
-  return polynomial;
+        var Y = 0, n0 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx2, dy2) {
+            n0++;
+            Y += (dy2 - Y) / n0;
+            if (!domain) {
+                if (dx2 < xmin)
+                    xmin = dx2;
+                if (dx2 > xmax)
+                    xmax = dx2;
+            }
+        });
+        var X2X2 = X4 - (X2 * X2);
+        var d = (X2 * X2X2 - X3 * X3);
+        var a = (X2Y * X2 - XY * X3) / d;
+        var b = (XY * X2X2 - X2Y * X3) / d;
+        var c = -a * X2;
+        var fn = function (xx) {
+            var shifted = xx - ux;
+            return a * shifted * shifted + b * shifted + c + uy;
+        };
+        var out = interpose(xmin, xmax, fn);
+        out.a = a;
+        out.b = b - 2 * a * ux;
+        out.c = c - b * ux + a * ux * ux + uy;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    quadraticRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return quadraticRegression;
+    };
+    quadraticRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return quadraticRegression;
+    };
+    quadraticRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return quadraticRegression;
+    };
+    return quadraticRegression;
 }
 
+// Adapted from regression-js by Tom Alexander
+function polynomial() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, order = 3, domain;
+    var polynomialRegression = function polynomialRegression(data) {
+        // Shortcut for lower-order polynomials:
+        if (order === 1) {
+            var o = linear().x(x).y(y).domain(domain)(data);
+            var result = [o[0], o[1]];
+            result.coefficients = [o.b, o.a];
+            result.predict = o.predict;
+            result.rSquared = o.rSquared;
+            return result;
+        }
+        if (order === 2) {
+            var o = quadratic().x(x).y(y).domain(domain)(data);
+            var result = [o[0], o[1]];
+            result.coefficients = [o.c, o.b, o.a];
+            result.predict = o.predict;
+            result.rSquared = o.rSquared;
+            return result;
+        }
+        var _a = points(data, x, y), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+        var n = xv.length;
+        var k = order + 1;
+        var lhs = [];
+        var rhs = [];
+        var Y = 0, n0 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            n0++;
+            Y += (dy - Y) / n0;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        // Build normal equations
+        for (var i = 0; i < k; i++) {
+            // LHS
+            var v = 0;
+            for (var l = 0; l < n; l++) {
+                v += Math.pow(xv[l], i) * yv[l];
+            }
+            lhs.push(v);
+            // RHS
+            var c = new Float64Array(k);
+            for (var j = 0; j < k; j++) {
+                var v2 = 0;
+                for (var l = 0; l < n; l++) {
+                    v2 += Math.pow(xv[l], i + j);
+                }
+                c[j] = v2;
+            }
+            rhs.push(c);
+        }
+        rhs.push(new Float64Array(lhs));
+        var coef = gaussianElimination(rhs);
+        var fn = function (xx) {
+            var shifted = xx - ux;
+            var val = uy + coef[0];
+            for (var i = 1; i < k; i++) {
+                val += coef[i] * Math.pow(shifted, i);
+            }
+            return val;
+        };
+        var out = interpose(xmin, xmax, fn);
+        out.coefficients = uncenter(k, coef, -ux, uy);
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, Y, fn);
+        return out;
+    };
+    polynomialRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return polynomialRegression;
+    };
+    polynomialRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return polynomialRegression;
+    };
+    polynomialRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return polynomialRegression;
+    };
+    polynomialRegression.order = function (n) {
+        if (!arguments.length)
+            return order;
+        order = n;
+        return polynomialRegression;
+    };
+    return polynomialRegression;
+}
 function uncenter(k, a, x, y) {
-  var z = Array(k);
-  var i, j, v, c; // initialize to zero
-
-  for (i = 0; i < k; ++i) {
-    z[i] = 0;
-  } // polynomial expansion
-
-
-  for (i = k - 1; i >= 0; --i) {
-    v = a[i];
-    c = 1;
-    z[i] += v;
-
-    for (j = 1; j <= i; ++j) {
-      c *= (i + 1 - j) / j; // binomial coefficent
-
-      z[i - j] += v * Math.pow(x, j) * c;
+    var z = new Array(k).fill(0);
+    for (var i = k - 1; i >= 0; --i) {
+        var v = a[i];
+        z[i] += v;
+        var c = 1;
+        for (var j = 1; j <= i; ++j) {
+            c *= (i + 1 - j) / j; // binomial coefficient
+            z[i - j] += v * Math.pow(x, j) * c;
+        }
     }
-  } // bias term
-
-
-  z[0] += y;
-  return z;
-} // Given an array for a two-dimensional matrix and the polynomial order,
-// solve A * x = b using Gaussian elimination.
-
-
+    // bias term
+    z[0] += y;
+    return z;
+}
+// Solve A * x = b using Gaussian elimination
 function gaussianElimination(matrix) {
-  var n = matrix.length - 1,
-      coef = [];
-  var i, j, k, r, t;
-
-  for (i = 0; i < n; ++i) {
-    r = i; // max row
-
-    for (j = i + 1; j < n; ++j) {
-      if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
-        r = j;
-      }
-    }
-
-    for (k = i; k < n + 1; ++k) {
-      t = matrix[k][i];
-      matrix[k][i] = matrix[k][r];
-      matrix[k][r] = t;
-    }
-
-    for (j = i + 1; j < n; ++j) {
-      for (k = n; k >= i; k--) {
-        matrix[k][j] -= matrix[k][i] * matrix[i][j] / matrix[i][i];
-      }
+    var n = matrix.length - 1;
+    var coef = new Array(n);
+    for (var i = 0; i < n; i++) {
+        var r = i;
+        // find pivot row
+        for (var j = i + 1; j < n; j++) {
+            if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
+                r = j;
+            }
+        }
+        // swap columns
+        for (var k = i; k < n + 1; k++) {
+            var t = matrix[k][i];
+            matrix[k][i] = matrix[k][r];
+            matrix[k][r] = t;
+        }
+        // reduce
+        for (var j = i + 1; j < n; j++) {
+            for (var k = n; k >= i; k--) {
+                matrix[k][j] -= (matrix[k][i] * matrix[i][j]) / matrix[i][i];
+            }
+        }
     }
-  }
-
-  for (j = n - 1; j >= 0; --j) {
-    t = 0;
-
-    for (k = j + 1; k < n; ++k) {
-      t += matrix[k][j] * coef[k];
+    for (var j = n - 1; j >= 0; j--) {
+        var t = 0;
+        for (var k = j + 1; k < n; k++) {
+            t += matrix[k][j] * coef[k];
+        }
+        coef[j] = (matrix[n][j] - t) / matrix[j][j];
     }
-
-    coef[j] = (matrix[n][j] - t) / matrix[j][j];
-  }
-
-  return coef;
+    return coef;
 }
 
-function power () {
-  var x = function x(d) {
-    return d[0];
-  },
-      y = function y(d) {
-    return d[1];
-  },
-      domain;
-
-  function power(data) {
-    var n = 0,
-        X = 0,
-        Y = 0,
-        XY = 0,
-        X2 = 0,
-        YS = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    visitPoints(data, x, y, function (dx, dy) {
-      var lx = Math.log(dx),
-          ly = Math.log(dy);
-      ++n;
-      X += (lx - X) / n;
-      Y += (ly - Y) / n;
-      XY += (lx * ly - XY) / n;
-      X2 += (lx * lx - X2) / n;
-      YS += (dy - YS) / n;
-
-      if (!domain) {
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    var _ols = ols(X, Y, XY, X2),
-        _ols2 = _slicedToArray(_ols, 2),
-        a = _ols2[0],
-        b = _ols2[1];
-
-    a = Math.exp(a);
-
-    var fn = function fn(x) {
-      return a * Math.pow(x, b);
-    },
-        out = interpose(xmin, xmax, fn);
-
-    out.a = a;
-    out.b = b;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, YS, fn);
-    return out;
-  }
-
-  power.domain = function (arr) {
-    return arguments.length ? (domain = arr, power) : domain;
-  };
-
-  power.x = function (fn) {
-    return arguments.length ? (x = fn, power) : x;
-  };
-
-  power.y = function (fn) {
-    return arguments.length ? (y = fn, power) : y;
-  };
+function power() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+    var powerRegression = function powerRegression(data) {
+        var n = 0, X = 0, Y = 0, XY = 0, X2 = 0, YS = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+        visitPoints(data, x, y, function (dx, dy) {
+            var lx = Math.log(dx), ly = Math.log(dy);
+            ++n;
+            X += (lx - X) / n;
+            Y += (ly - Y) / n;
+            XY += (lx * ly - XY) / n;
+            X2 += (lx * lx - X2) / n;
+            YS += (dy - YS) / n;
+            if (!domain) {
+                if (dx < xmin)
+                    xmin = dx;
+                if (dx > xmax)
+                    xmax = dx;
+            }
+        });
+        var _a = ols(X, Y, XY, X2), a = _a[0], b = _a[1];
+        a = Math.exp(a);
+        var fn = function (xx) { return a * Math.pow(xx, b); };
+        var out = interpose(xmin, xmax, fn);
+        out.a = a;
+        out.b = b;
+        out.predict = fn;
+        out.rSquared = determination(data, x, y, YS, fn);
+        return out;
+    };
+    powerRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return powerRegression;
+    };
+    powerRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return powerRegression;
+    };
+    powerRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return powerRegression;
+    };
+    return powerRegression;
+}
 
-  return power;
+function sigmoidal() {
+    var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain, maxIter = 2000, alpha = 1e-4; // learning rate
+    function sigmoidalRegression(data) {
+        var n = 0, Xmin = domain ? +domain[0] : Infinity, Xmax = domain ? +domain[1] : -Infinity, Ymin = Infinity, Ymax = -Infinity, sumY = 0;
+        // Gather data & track min/max for domain if not preset
+        visitPoints(data, x, y, function (dx, dy) {
+            n++;
+            sumY += dy;
+            if (!domain) {
+                if (dx < Xmin)
+                    Xmin = dx;
+                if (dx > Xmax)
+                    Xmax = dx;
+            }
+            if (dy < Ymin)
+                Ymin = dy;
+            if (dy > Ymax)
+                Ymax = dy;
+        });
+        if (!domain && (Xmin === Infinity || Xmax === -Infinity)) {
+            Xmin = 0;
+            Xmax = 1;
+        }
+        // Initialize parameters
+        var A = Ymax - Ymin; // amplitude
+        var B = 1; // logistic slope
+        var C = Ymin; // baseline offset
+        var M = (Xmin + Xmax) / 2; // midpoint
+        // Predict function
+        function f(xx) {
+            return C + A / (1 + Math.exp(-B * (xx - M)));
+        }
+        var _loop_1 = function (iter) {
+            var dA = 0, dB = 0, dC = 0, dM = 0;
+            visitPoints(data, x, y, function (dx, dy) {
+                var yhat = f(dx);
+                var err = dy - yhat;
+                var ex = Math.exp(-B * (dx - M));
+                var g = 1 / (1 + ex); // logistic
+                // partial derivatives
+                var df_dA = g;
+                var df_dC = 1;
+                var df_dB = A * g * (1 - g) * (dx - M);
+                // Note the negative sign for M:
+                var df_dM = -A * B * g * (1 - g);
+                var factor = -2 * err;
+                dA += factor * df_dA;
+                dB += factor * df_dB;
+                dC += factor * df_dC;
+                dM += factor * df_dM;
+            });
+            A -= alpha * (dA / n);
+            B -= alpha * (dB / n);
+            C -= alpha * (dC / n);
+            M -= alpha * (dM / n);
+        };
+        // Gradient Descent
+        for (var iter = 0; iter < maxIter; iter++) {
+            _loop_1();
+        }
+        var predict = function (xx) { return C + A / (1 + Math.exp(-B * (xx - M))); };
+        var out = interpose(Xmin, Xmax, predict);
+        out.A = A;
+        out.B = B;
+        out.C = C;
+        out.M = M;
+        out.predict = predict;
+        // R^2
+        var meanY = sumY / n;
+        out.rSquared = determination(data, x, y, meanY, predict);
+        return out;
+    }
+    sigmoidalRegression.domain = function (arr) {
+        if (!arguments.length)
+            return domain;
+        domain = arr;
+        return sigmoidalRegression;
+    };
+    sigmoidalRegression.x = function (fn) {
+        if (!arguments.length)
+            return x;
+        x = fn;
+        return sigmoidalRegression;
+    };
+    sigmoidalRegression.y = function (fn) {
+        if (!arguments.length)
+            return y;
+        y = fn;
+        return sigmoidalRegression;
+    };
+    return sigmoidalRegression;
 }
 
-export { exponential as regressionExp, linear as regressionLinear, loess as regressionLoess, logarithmic as regressionLog, polynomial as regressionPoly, power as regressionPow, quad as regressionQuad };
+export { exponential as regressionExp, linear as regressionLinear, loess as regressionLoess, logarithmic as regressionLog, polynomial as regressionPoly, power as regressionPow, quadratic as regressionQuad, sigmoidal as regressionSigmoidal };
diff --git a/dist/d3-regression.js b/dist/d3-regression.js
index 6437bff..f6b0111 100644
--- a/dist/d3-regression.js
+++ b/dist/d3-regression.js
@@ -1,875 +1,766 @@
-// https://github.com/HarryStevens/d3-regression#readme Version 1.3.10. Copyright 2022 Harry Stevens.
+// https://github.com/HarryStevens/d3-regression#readme Version 1.3.10. Copyright 2025 Harry Stevens.
 (function (global, factory) {
   typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
   typeof define === 'function' && define.amd ? define(['exports'], factory) :
   (global = global || self, factory(global.d3 = {}));
 }(this, (function (exports) { 'use strict';
 
-  function _slicedToArray(arr, i) {
-    return _arrayWithHoles(arr) || _iterableToArrayLimit(arr, i) || _nonIterableRest();
-  }
-
-  function _arrayWithHoles(arr) {
-    if (Array.isArray(arr)) return arr;
-  }
-
-  function _iterableToArrayLimit(arr, i) {
-    var _arr = [];
-    var _n = true;
-    var _d = false;
-    var _e = undefined;
-
-    try {
-      for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) {
-        _arr.push(_s.value);
-
-        if (i && _arr.length === i) break;
-      }
-    } catch (err) {
-      _d = true;
-      _e = err;
-    } finally {
-      try {
-        if (!_n && _i["return"] != null) _i["return"]();
-      } finally {
-        if (_d) throw _e;
-      }
-    }
-
-    return _arr;
-  }
-
-  function _nonIterableRest() {
-    throw new TypeError("Invalid attempt to destructure non-iterable instance");
-  }
-
-  // Adapted from vega-statistics by Jeffrey Heer
-  // License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-  // Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+  /**
+   * Adapted from vega-statistics by Jeffrey Heer
+   * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+   * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+   */
   function points(data, x, y, sort) {
-    data = data.filter(function (d, i) {
-      var u = x(d, i),
-          v = y(d, i);
-      return u != null && isFinite(u) && v != null && isFinite(v);
-    });
-
-    if (sort) {
-      data.sort(function (a, b) {
-        return x(a) - x(b);
+      data = data.filter(function (d, i) {
+          var u = x(d, i), v = y(d, i);
+          return u != null && isFinite(u) && v != null && isFinite(v);
       });
-    }
-
-    var n = data.length,
-        X = new Float64Array(n),
-        Y = new Float64Array(n); // extract values, calculate means
-
-    var ux = 0,
-        uy = 0,
-        xv,
-        yv,
-        d;
-
-    for (var i = 0; i < n;) {
-      d = data[i];
-      X[i] = xv = +x(d, i, data);
-      Y[i] = yv = +y(d, i, data);
-      ++i;
-      ux += (xv - ux) / i;
-      uy += (yv - uy) / i;
-    } // mean center the data
-
-
-    for (var _i = 0; _i < n; ++_i) {
-      X[_i] -= ux;
-      Y[_i] -= uy;
-    }
-
-    return [X, Y, ux, uy];
+      if (sort) {
+          data.sort(function (a, b) { return x(a) - x(b); });
+      }
+      var n = data.length, X = new Float64Array(n), Y = new Float64Array(n);
+      // extract values, calculate means
+      var ux = 0, uy = 0, xv, yv, d;
+      for (var i = 0; i < n;) {
+          d = data[i];
+          X[i] = xv = +x(d, i, data);
+          Y[i] = yv = +y(d, i, data);
+          ++i;
+          ux += (xv - ux) / i;
+          uy += (yv - uy) / i;
+      }
+      // mean center the data
+      for (var i = 0; i < n; ++i) {
+          X[i] -= ux;
+          Y[i] -= uy;
+      }
+      return [X, Y, ux, uy];
   }
+  /**
+   * Iterates over valid data points, invoking a callback for each.
+   */
   function visitPoints(data, x, y, cb) {
-    var iterations = 0;
-
-    for (var i = 0, n = data.length; i < n; i++) {
-      var d = data[i],
-          dx = +x(d, i, data),
-          dy = +y(d, i, data);
-
-      if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
-        cb(dx, dy, iterations++);
+      var iterations = 0;
+      for (var i = 0; i < data.length; i++) {
+          var d = data[i];
+          var dx = +x(d, i, data);
+          var dy = +y(d, i, data);
+          if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
+              cb(dx, dy, iterations++);
+          }
       }
-    }
   }
 
-  // return the coefficient of determination, or R squared.
-
+  /**
+   * Given a dataset, x- and y-accessors, the mean center of the y-values (uY),
+   * and a prediction function, return the coefficient of determination, R^2.
+   */
   function determination(data, x, y, uY, predict) {
-    var SSE = 0,
-        SST = 0;
-    visitPoints(data, x, y, function (dx, dy) {
-      var sse = dy - predict(dx),
-          sst = dy - uY;
-      SSE += sse * sse;
-      SST += sst * sst;
-    });
-    return 1 - SSE / SST;
+      var SSE = 0, // Sum of Squared Errors
+      SST = 0; // Total Sum of Squares
+      visitPoints(data, x, y, function (dx, dy) {
+          var sse = dy - predict(dx);
+          var sst = dy - uY;
+          SSE += sse * sse;
+          SST += sst * sst;
+      });
+      return 1 - SSE / SST;
   }
 
-  // Returns the angle of a line in degrees.
+  /**
+   * Returns the angle of a line in degrees.
+   */
   function angle(line) {
-    return Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) * 180 / Math.PI;
-  } // Returns the midpoint of a line.
-
+      return (Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) *
+          (180 / Math.PI));
+  }
+  /**
+   * Returns the midpoint of a line.
+   */
   function midpoint(line) {
-    return [(line[0][0] + line[1][0]) / 2, (line[0][1] + line[1][1]) / 2];
+      return [
+          (line[0][0] + line[1][0]) / 2,
+          (line[0][1] + line[1][1]) / 2
+      ];
   }
 
-  // returns a smooth line.
-
+  /**
+   * Given a start point (xmin), an end point (xmax),
+   * and a prediction function, returns a smooth line.
+   */
   function interpose(xmin, xmax, predict) {
-    var l = Math.log(xmax - xmin) * Math.LOG10E + 1 | 0;
-    var precision = 1 * Math.pow(10, -l / 2 - 1),
-        maxIter = 1e4;
-    var points = [px(xmin), px(xmax)],
-        iter = 0;
-
-    while (find(points) && iter < maxIter) {
-    }
-
-    return points;
-
-    function px(x) {
-      return [x, predict(x)];
-    }
-
-    function find(points) {
-      iter++;
-      var n = points.length;
-      var found = false;
-
-      for (var i = 0; i < n - 1; i++) {
-        var p0 = points[i],
-            p1 = points[i + 1],
-            m = midpoint([p0, p1]),
-            mp = px(m[0]),
-            a0 = angle([p0, m]),
-            a1 = angle([p0, mp]),
-            a = Math.abs(a0 - a1);
-
-        if (a > precision) {
-          points.splice(i + 1, 0, mp);
-          found = true;
-        }
+      var l = (Math.log(xmax - xmin) * Math.LOG10E + 1) | 0;
+      var precision = Math.pow(10, -l / 2 - 1);
+      var maxIter = 1e4;
+      var points = [px(xmin), px(xmax)];
+      var iter = 0;
+      while (find(points) && iter < maxIter)
+          ;
+      return points;
+      function px(x) {
+          return [x, predict(x)];
+      }
+      function find(points) {
+          iter++;
+          var n = points.length;
+          var found = false;
+          for (var i = 0; i < n - 1; i++) {
+              var p0 = points[i];
+              var p1 = points[i + 1];
+              var m = midpoint([p0, p1]);
+              var mp = px(m[0]);
+              var a0 = angle([p0, m]);
+              var a1 = angle([p0, mp]);
+              var a = Math.abs(a0 - a1);
+              if (a > precision) {
+                  points.splice(i + 1, 0, mp);
+                  found = true;
+              }
+          }
+          return found;
       }
-
-      return found;
-    }
   }
 
-  // Ordinary Least Squares from vega-statistics by Jeffrey Heer
-  // License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-  // Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+  /**
+   * Ordinary Least Squares from vega-statistics by Jeffrey Heer
+   * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+   * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+   */
   function ols(uX, uY, uXY, uX2) {
-    var delta = uX2 - uX * uX,
-        slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta,
-        intercept = uY - slope * uX;
-    return [intercept, slope];
+      var delta = uX2 - uX * uX, slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta, intercept = uY - slope * uX;
+      return [intercept, slope];
   }
 
-  function exponential () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        domain;
-
-    function exponential(data) {
-      var n = 0,
-          Y = 0,
-          YL = 0,
-          XY = 0,
-          XYL = 0,
-          X2Y = 0,
-          xmin = domain ? +domain[0] : Infinity,
-          xmax = domain ? +domain[1] : -Infinity;
-      visitPoints(data, x, y, function (dx, dy) {
-        var ly = Math.log(dy),
-            xy = dx * dy;
-        ++n;
-        Y += (dy - Y) / n;
-        XY += (xy - XY) / n;
-        X2Y += (dx * xy - X2Y) / n;
-        YL += (dy * ly - YL) / n;
-        XYL += (xy * ly - XYL) / n;
-
-        if (!domain) {
-          if (dx < xmin) xmin = dx;
-          if (dx > xmax) xmax = dx;
-        }
-      });
-
-      var _ols = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y),
-          _ols2 = _slicedToArray(_ols, 2),
-          a = _ols2[0],
-          b = _ols2[1];
-
-      a = Math.exp(a);
-
-      var fn = function fn(x) {
-        return a * Math.exp(b * x);
-      },
-          out = interpose(xmin, xmax, fn);
-
-      out.a = a;
-      out.b = b;
-      out.predict = fn;
-      out.rSquared = determination(data, x, y, Y, fn);
-      return out;
-    }
-
-    exponential.domain = function (arr) {
-      return arguments.length ? (domain = arr, exponential) : domain;
-    };
-
-    exponential.x = function (fn) {
-      return arguments.length ? (x = fn, exponential) : x;
-    };
-
-    exponential.y = function (fn) {
-      return arguments.length ? (y = fn, exponential) : y;
-    };
-
-    return exponential;
+  function exponential() {
+      var y = function (d) { return d[1]; }, x = function (d) { return d[0]; }, domain;
+      var exponentialRegression = function (data) {
+          var n = 0, Y = 0, YL = 0, XY = 0, XYL = 0, X2Y = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+          visitPoints(data, x, y, function (dx, dy) {
+              var ly = Math.log(dy), xy = dx * dy;
+              ++n;
+              Y += (dy - Y) / n;
+              XY += (xy - XY) / n;
+              X2Y += (dx * xy - X2Y) / n;
+              YL += (dy * ly - YL) / n;
+              XYL += (xy * ly - XYL) / n;
+              if (!domain) {
+                  if (dx < xmin)
+                      xmin = dx;
+                  if (dx > xmax)
+                      xmax = dx;
+              }
+          });
+          var _a = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y), a = _a[0], b = _a[1];
+          a = Math.exp(a);
+          var fn = function (xx) { return a * Math.exp(b * xx); };
+          var out = interpose(xmin, xmax, fn);
+          out.a = a;
+          out.b = b;
+          out.predict = fn;
+          out.rSquared = determination(data, x, y, Y, fn);
+          return out;
+      };
+      exponentialRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return exponentialRegression;
+      };
+      exponentialRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return exponentialRegression;
+      };
+      exponentialRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return exponentialRegression;
+      };
+      return exponentialRegression;
   }
 
-  function linear () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        domain;
-
-    function linear(data) {
-      var n = 0,
-          X = 0,
-          // sum of x
-      Y = 0,
-          // sum of y
-      XY = 0,
-          // sum of x * y
-      X2 = 0,
-          // sum of x * x
-      xmin = domain ? +domain[0] : Infinity,
-          xmax = domain ? +domain[1] : -Infinity;
-      visitPoints(data, x, y, function (dx, dy) {
-        ++n;
-        X += (dx - X) / n;
-        Y += (dy - Y) / n;
-        XY += (dx * dy - XY) / n;
-        X2 += (dx * dx - X2) / n;
-
-        if (!domain) {
-          if (dx < xmin) xmin = dx;
-          if (dx > xmax) xmax = dx;
-        }
-      });
-
-      var _ols = ols(X, Y, XY, X2),
-          _ols2 = _slicedToArray(_ols, 2),
-          intercept = _ols2[0],
-          slope = _ols2[1],
-          fn = function fn(x) {
-        return slope * x + intercept;
-      },
-          out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
-
-      out.a = slope;
-      out.b = intercept;
-      out.predict = fn;
-      out.rSquared = determination(data, x, y, Y, fn);
-      return out;
-    }
-
-    linear.domain = function (arr) {
-      return arguments.length ? (domain = arr, linear) : domain;
-    };
-
-    linear.x = function (fn) {
-      return arguments.length ? (x = fn, linear) : x;
-    };
-
-    linear.y = function (fn) {
-      return arguments.length ? (y = fn, linear) : y;
-    };
-
-    return linear;
+  function linear() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+      var linearRegression = function (data) {
+          var n = 0, X = 0, // sum of x
+          Y = 0, // sum of y
+          XY = 0, // sum of x*y
+          X2 = 0, // sum of x*x
+          xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+          visitPoints(data, x, y, function (dx, dy) {
+              ++n;
+              X += (dx - X) / n;
+              Y += (dy - Y) / n;
+              XY += (dx * dy - XY) / n;
+              X2 += (dx * dx - X2) / n;
+              if (!domain) {
+                  if (dx < xmin)
+                      xmin = dx;
+                  if (dx > xmax)
+                      xmax = dx;
+              }
+          });
+          var _a = ols(X, Y, XY, X2), intercept = _a[0], slope = _a[1];
+          var fn = function (xx) { return slope * xx + intercept; };
+          var out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
+          out.a = slope;
+          out.b = intercept;
+          out.predict = fn;
+          out.rSquared = determination(data, x, y, Y, fn);
+          return out;
+      };
+      linearRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return linearRegression;
+      };
+      linearRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return linearRegression;
+      };
+      linearRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return linearRegression;
+      };
+      return linearRegression;
   }
 
-  // Returns the medium value of an array of numbers.
+  /**
+   * Returns the median value of an array of numbers.
+   */
   function median(arr) {
-    arr.sort(function (a, b) {
-      return a - b;
-    });
-    var i = arr.length / 2;
-    return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
+      arr.sort(function (a, b) { return a - b; });
+      var i = arr.length / 2;
+      return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
   }
 
-  var maxiters = 2,
-      epsilon = 1e-12;
-  function loess () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        bandwidth = .3;
-
-    function loess(data) {
-      var _points = points(data, x, y, true),
-          _points2 = _slicedToArray(_points, 4),
-          xv = _points2[0],
-          yv = _points2[1],
-          ux = _points2[2],
-          uy = _points2[3],
-          n = xv.length,
-          bw = Math.max(2, ~~(bandwidth * n)),
-          yhat = new Float64Array(n),
-          residuals = new Float64Array(n),
-          robustWeights = new Float64Array(n).fill(1);
-
-      for (var iter = -1; ++iter <= maxiters;) {
-        var interval = [0, bw - 1];
-
-        for (var i = 0; i < n; ++i) {
-          var dx = xv[i],
-              i0 = interval[0],
-              i1 = interval[1],
-              edge = dx - xv[i0] > xv[i1] - dx ? i0 : i1;
-          var W = 0,
-              X = 0,
-              Y = 0,
-              XY = 0,
-              X2 = 0,
-              denom = 1 / Math.abs(xv[edge] - dx || 1); // Avoid singularity
-
-          for (var k = i0; k <= i1; ++k) {
-            var xk = xv[k],
-                yk = yv[k],
-                w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k],
-                xkw = xk * w;
-            W += w;
-            X += xkw;
-            Y += yk * w;
-            XY += yk * xkw;
-            X2 += xk * xkw;
-          } // Linear regression fit
-
-
-          var _ols = ols(X / W, Y / W, XY / W, X2 / W),
-              _ols2 = _slicedToArray(_ols, 2),
-              a = _ols2[0],
-              b = _ols2[1];
-
-          yhat[i] = a + b * dx;
-          residuals[i] = Math.abs(yv[i] - yhat[i]);
-          updateInterval(xv, i + 1, interval);
-        }
-
-        if (iter === maxiters) {
-          break;
-        }
-
-        var medianResidual = median(residuals);
-        if (Math.abs(medianResidual) < epsilon) break;
-
-        for (var _i = 0, arg, _w; _i < n; ++_i) {
-          arg = residuals[_i] / (6 * medianResidual); // Default to epsilon (rather than zero) for large deviations
-          // Keeping weights tiny but non-zero prevents singularites
-
-          robustWeights[_i] = arg >= 1 ? epsilon : (_w = 1 - arg * arg) * _w;
-        }
-      }
-
-      return output(xv, yhat, ux, uy);
-    }
-
-    loess.bandwidth = function (bw) {
-      return arguments.length ? (bandwidth = bw, loess) : bandwidth;
-    };
-
-    loess.x = function (fn) {
-      return arguments.length ? (x = fn, loess) : x;
-    };
-
-    loess.y = function (fn) {
-      return arguments.length ? (y = fn, loess) : y;
-    };
-
-    return loess;
-  } // Weighting kernel for local regression
-
+  // Adapted from science.js by Jason Davies
+  var maxiters = 2, epsilon = 1e-12;
+  function loess() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, bandwidth = 0.3;
+      var loessRegression = function loessRegression(data) {
+          var _a = points(data, function (dd) { return x(dd); }, function (dd) { return y(dd); }, true), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+          var n = xv.length;
+          var bw = Math.max(2, ~~(bandwidth * n)); // # of nearest neighbors
+          var yhat = new Float64Array(n);
+          var residuals = new Float64Array(n);
+          var robustWeights = new Float64Array(n).fill(1);
+          for (var iter = -1; ++iter <= maxiters;) {
+              var interval = [0, bw - 1];
+              for (var i = 0; i < n; ++i) {
+                  var dx = xv[i];
+                  var i0 = interval[0];
+                  var i1 = interval[1];
+                  var edge = (dx - xv[i0]) > (xv[i1] - dx) ? i0 : i1;
+                  var W = 0, X = 0, Y = 0, XY = 0, X2 = 0;
+                  var denom = 1 / Math.abs(xv[edge] - dx || 1);
+                  for (var k = i0; k <= i1; ++k) {
+                      var xk = xv[k];
+                      var yk = yv[k];
+                      var w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k];
+                      var xkw = xk * w;
+                      W += w;
+                      X += xkw;
+                      Y += yk * w;
+                      XY += yk * xkw;
+                      X2 += xk * xkw;
+                  }
+                  // Linear regression fit
+                  var _b = ols(X / W, Y / W, XY / W, X2 / W), a = _b[0], b = _b[1];
+                  yhat[i] = a + b * dx;
+                  residuals[i] = Math.abs(yv[i] - yhat[i]);
+                  updateInterval(xv, i + 1, interval);
+              }
+              if (iter === maxiters) {
+                  break;
+              }
+              var medianResidual = median(residuals);
+              if (Math.abs(medianResidual) < epsilon)
+                  break;
+              for (var i = 0, arg = void 0, w = void 0; i < n; ++i) {
+                  arg = residuals[i] / (6 * medianResidual);
+                  // Default to epsilon (rather than zero) for large deviations
+                  // Keeping weights tiny but non-zero prevents singularites
+                  robustWeights[i] = (arg >= 1) ? epsilon : ((w = 1 - arg * arg) * w);
+              }
+          }
+          return output(xv, yhat, ux, uy);
+      };
+      loessRegression.bandwidth = function (bw) {
+          if (!arguments.length)
+              return bandwidth;
+          bandwidth = bw;
+          return loessRegression;
+      };
+      loessRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return loessRegression;
+      };
+      loessRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return loessRegression;
+      };
+      return loessRegression;
+  }
+  // Weighting kernel for local regression
   function tricube(x) {
-    return (x = 1 - x * x * x) * x * x;
-  } // Advance sliding window interval of nearest neighbors
-
-
+      return (x = 1 - x * x * x) * x * x;
+  }
+  // Advance sliding window interval of nearest neighbors
   function updateInterval(xv, i, interval) {
-    var val = xv[i],
-        left = interval[0],
-        right = interval[1] + 1;
-    if (right >= xv.length) return; // Step right if distance to new right edge is <= distance to old left edge
-    // Step when distance is equal to ensure movement over duplicate x values
-
-    while (i > left && xv[right] - val <= val - xv[left]) {
-      interval[0] = ++left;
-      interval[1] = right;
-      ++right;
-    }
-  } // Generate smoothed output points
+      var val = xv[i], left = interval[0], right = interval[1] + 1;
+      if (right >= xv.length)
+          return;
+      // Step right if distance to new right edge is <= distance to old left edge
+      // Step when distance is equal to ensure movement over duplicate x values
+      while (i > left && (xv[right] - val) <= (val - xv[left])) {
+          interval[0] = ++left;
+          interval[1] = right;
+          ++right;
+      }
+  }
+  // Generate smoothed output points
   // Average points with repeated x values
-
-
   function output(xv, yhat, ux, uy) {
-    var n = xv.length,
-        out = [];
-    var i = 0,
-        cnt = 0,
-        prev = [],
-        v;
-
-    for (; i < n; ++i) {
-      v = xv[i] + ux;
-
-      if (prev[0] === v) {
-        // Average output values via online update
-        prev[1] += (yhat[i] - prev[1]) / ++cnt;
-      } else {
-        // Add new output point
-        cnt = 0;
-        prev[1] += uy;
-        prev = [v, yhat[i]];
-        out.push(prev);
+      var n = xv.length, out = [];
+      var i = 0, cnt = 0, prev = [], v;
+      for (; i < n; ++i) {
+          v = xv[i] + ux;
+          if (prev[0] === v) {
+              // Average output values via online update
+              prev[1] += (yhat[i] - prev[1]) / (++cnt);
+          }
+          else {
+              // Add new output point
+              cnt = 0;
+              prev[1] += uy;
+              prev = [v, yhat[i]];
+              out.push(prev);
+          }
       }
-    }
-
-    prev[1] += uy;
-    return out;
-  }
-
-  function logarithmic () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        base = Math.E,
-        domain;
-
-    function logarithmic(data) {
-      var n = 0,
-          X = 0,
-          Y = 0,
-          XY = 0,
-          X2 = 0,
-          xmin = domain ? +domain[0] : Infinity,
-          xmax = domain ? +domain[1] : -Infinity,
-          lb = Math.log(base);
-      visitPoints(data, x, y, function (dx, dy) {
-        var lx = Math.log(dx) / lb;
-        ++n;
-        X += (lx - X) / n;
-        Y += (dy - Y) / n;
-        XY += (lx * dy - XY) / n;
-        X2 += (lx * lx - X2) / n;
-
-        if (!domain) {
-          if (dx < xmin) xmin = dx;
-          if (dx > xmax) xmax = dx;
-        }
-      });
-
-      var _ols = ols(X, Y, XY, X2),
-          _ols2 = _slicedToArray(_ols, 2),
-          intercept = _ols2[0],
-          slope = _ols2[1],
-          fn = function fn(x) {
-        return slope * Math.log(x) / lb + intercept;
-      },
-          out = interpose(xmin, xmax, fn);
-
-      out.a = slope;
-      out.b = intercept;
-      out.predict = fn;
-      out.rSquared = determination(data, x, y, Y, fn);
+      prev[1] += uy;
       return out;
-    }
-
-    logarithmic.domain = function (arr) {
-      return arguments.length ? (domain = arr, logarithmic) : domain;
-    };
-
-    logarithmic.x = function (fn) {
-      return arguments.length ? (x = fn, logarithmic) : x;
-    };
-
-    logarithmic.y = function (fn) {
-      return arguments.length ? (y = fn, logarithmic) : y;
-    };
-
-    logarithmic.base = function (n) {
-      return arguments.length ? (base = n, logarithmic) : base;
-    };
-
-    return logarithmic;
   }
 
-  function quad () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        domain;
-
-    function quadratic(data) {
-      var _points = points(data, x, y),
-          _points2 = _slicedToArray(_points, 4),
-          xv = _points2[0],
-          yv = _points2[1],
-          ux = _points2[2],
-          uy = _points2[3],
-          n = xv.length;
-
-      var X2 = 0,
-          X3 = 0,
-          X4 = 0,
-          XY = 0,
-          X2Y = 0,
-          i,
-          dx,
-          dy,
-          x2;
-
-      for (i = 0; i < n;) {
-        dx = xv[i];
-        dy = yv[i++];
-        x2 = dx * dx;
-        X2 += (x2 - X2) / i;
-        X3 += (x2 * dx - X3) / i;
-        X4 += (x2 * x2 - X4) / i;
-        XY += (dx * dy - XY) / i;
-        X2Y += (x2 * dy - X2Y) / i;
-      }
-
-      var Y = 0,
-          n0 = 0,
-          xmin = domain ? +domain[0] : Infinity,
-          xmax = domain ? +domain[1] : -Infinity;
-      visitPoints(data, x, y, function (dx, dy) {
-        n0++;
-        Y += (dy - Y) / n0;
-
-        if (!domain) {
-          if (dx < xmin) xmin = dx;
-          if (dx > xmax) xmax = dx;
-        }
-      });
-
-      var X2X2 = X4 - X2 * X2,
-          d = X2 * X2X2 - X3 * X3,
-          a = (X2Y * X2 - XY * X3) / d,
-          b = (XY * X2X2 - X2Y * X3) / d,
-          c = -a * X2,
-          fn = function fn(x) {
-        x = x - ux;
-        return a * x * x + b * x + c + uy;
+  function logarithmic() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, base = Math.E, domain;
+      var logarithmicRegression = function (data) {
+          var n = 0, X = 0, Y = 0, XY = 0, X2 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity, lb = Math.log(base);
+          visitPoints(data, x, y, function (dx, dy) {
+              var lx = Math.log(dx) / lb;
+              ++n;
+              X += (lx - X) / n;
+              Y += (dy - Y) / n;
+              XY += (lx * dy - XY) / n;
+              X2 += (lx * lx - X2) / n;
+              if (!domain) {
+                  if (dx < xmin)
+                      xmin = dx;
+                  if (dx > xmax)
+                      xmax = dx;
+              }
+          });
+          var _a = ols(X, Y, XY, X2), intercept = _a[0], slope = _a[1];
+          var fn = function (xx) { return slope * Math.log(xx) / lb + intercept; };
+          var out = interpose(xmin, xmax, fn);
+          out.a = slope;
+          out.b = intercept;
+          out.predict = fn;
+          out.rSquared = determination(data, x, y, Y, fn);
+          return out;
       };
-
-      var out = interpose(xmin, xmax, fn);
-      out.a = a;
-      out.b = b - 2 * a * ux;
-      out.c = c - b * ux + a * ux * ux + uy;
-      out.predict = fn;
-      out.rSquared = determination(data, x, y, Y, fn);
-      return out;
-    }
-
-    quadratic.domain = function (arr) {
-      return arguments.length ? (domain = arr, quadratic) : domain;
-    };
-
-    quadratic.x = function (fn) {
-      return arguments.length ? (x = fn, quadratic) : x;
-    };
-
-    quadratic.y = function (fn) {
-      return arguments.length ? (y = fn, quadratic) : y;
-    };
-
-    return quadratic;
+      logarithmicRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return logarithmicRegression;
+      };
+      logarithmicRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return logarithmicRegression;
+      };
+      logarithmicRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return logarithmicRegression;
+      };
+      logarithmicRegression.base = function (b) {
+          if (!arguments.length)
+              return base;
+          base = b;
+          return logarithmicRegression;
+      };
+      return logarithmicRegression;
   }
 
-  // Source: https://github.com/Tom-Alexander/regression-js/blob/master/src/regression.js#L246
-  // License: https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE
-  // ...with ideas from vega-statistics by Jeffrey Heer
-  // Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/poly.js
-  // License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-
-  function polynomial () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        order = 3,
-        domain;
-
-    function polynomial(data) {
-      // Use more efficient methods for lower orders
-      if (order === 1) {
-        var o = linear().x(x).y(y).domain(domain)(data);
-        o.coefficients = [o.b, o.a];
-        delete o.a;
-        delete o.b;
-        return o;
-      }
-
-      if (order === 2) {
-        var _o = quad().x(x).y(y).domain(domain)(data);
-
-        _o.coefficients = [_o.c, _o.b, _o.a];
-        delete _o.a;
-        delete _o.b;
-        delete _o.c;
-        return _o;
-      }
-
-      var _points = points(data, x, y),
-          _points2 = _slicedToArray(_points, 4),
-          xv = _points2[0],
-          yv = _points2[1],
-          ux = _points2[2],
-          uy = _points2[3],
-          n = xv.length,
-          lhs = [],
-          rhs = [],
-          k = order + 1;
-
-      var Y = 0,
-          n0 = 0,
-          xmin = domain ? +domain[0] : Infinity,
-          xmax = domain ? +domain[1] : -Infinity;
-      visitPoints(data, x, y, function (dx, dy) {
-        ++n0;
-        Y += (dy - Y) / n0;
-
-        if (!domain) {
-          if (dx < xmin) xmin = dx;
-          if (dx > xmax) xmax = dx;
-        }
-      });
-      var i, j, l, v, c;
-
-      for (i = 0; i < k; ++i) {
-        for (l = 0, v = 0; l < n; ++l) {
-          v += Math.pow(xv[l], i) * yv[l];
-        }
-
-        lhs.push(v);
-        c = new Float64Array(k);
-
-        for (j = 0; j < k; ++j) {
-          for (l = 0, v = 0; l < n; ++l) {
-            v += Math.pow(xv[l], i + j);
+  function quadratic() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+      var quadraticRegression = function quadraticRegression(data) {
+          var _a = points(data, function (dd) { return x(dd); }, function (dd) { return y(dd); }), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+          var n = xv.length;
+          var X2 = 0, X3 = 0, X4 = 0, XY = 0, X2Y = 0, i, dx, dy, x2;
+          for (i = 0; i < n;) {
+              dx = xv[i];
+              dy = yv[i++];
+              x2 = dx * dx;
+              X2 += (x2 - X2) / i;
+              X3 += ((x2 * dx) - X3) / i;
+              X4 += ((x2 * x2) - X4) / i;
+              XY += ((dx * dy) - XY) / i;
+              X2Y += ((x2 * dy) - X2Y) / i;
           }
-
-          c[j] = v;
-        }
-
-        rhs.push(c);
-      }
-
-      rhs.push(lhs);
-
-      var coef = gaussianElimination(rhs),
-          fn = function fn(x) {
-        x -= ux;
-        var y = uy + coef[0] + coef[1] * x + coef[2] * x * x;
-
-        for (i = 3; i < k; ++i) {
-          y += coef[i] * Math.pow(x, i);
-        }
-
-        return y;
-      },
-          out = interpose(xmin, xmax, fn);
-
-      out.coefficients = uncenter(k, coef, -ux, uy);
-      out.predict = fn;
-      out.rSquared = determination(data, x, y, Y, fn);
-      return out;
-    }
-
-    polynomial.domain = function (arr) {
-      return arguments.length ? (domain = arr, polynomial) : domain;
-    };
-
-    polynomial.x = function (fn) {
-      return arguments.length ? (x = fn, polynomial) : x;
-    };
-
-    polynomial.y = function (fn) {
-      return arguments.length ? (y = fn, polynomial) : y;
-    };
-
-    polynomial.order = function (n) {
-      return arguments.length ? (order = n, polynomial) : order;
-    };
-
-    return polynomial;
+          var Y = 0, n0 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+          visitPoints(data, x, y, function (dx2, dy2) {
+              n0++;
+              Y += (dy2 - Y) / n0;
+              if (!domain) {
+                  if (dx2 < xmin)
+                      xmin = dx2;
+                  if (dx2 > xmax)
+                      xmax = dx2;
+              }
+          });
+          var X2X2 = X4 - (X2 * X2);
+          var d = (X2 * X2X2 - X3 * X3);
+          var a = (X2Y * X2 - XY * X3) / d;
+          var b = (XY * X2X2 - X2Y * X3) / d;
+          var c = -a * X2;
+          var fn = function (xx) {
+              var shifted = xx - ux;
+              return a * shifted * shifted + b * shifted + c + uy;
+          };
+          var out = interpose(xmin, xmax, fn);
+          out.a = a;
+          out.b = b - 2 * a * ux;
+          out.c = c - b * ux + a * ux * ux + uy;
+          out.predict = fn;
+          out.rSquared = determination(data, x, y, Y, fn);
+          return out;
+      };
+      quadraticRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return quadraticRegression;
+      };
+      quadraticRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return quadraticRegression;
+      };
+      quadraticRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return quadraticRegression;
+      };
+      return quadraticRegression;
   }
 
+  // Adapted from regression-js by Tom Alexander
+  function polynomial() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, order = 3, domain;
+      var polynomialRegression = function polynomialRegression(data) {
+          // Shortcut for lower-order polynomials:
+          if (order === 1) {
+              var o = linear().x(x).y(y).domain(domain)(data);
+              var result = [o[0], o[1]];
+              result.coefficients = [o.b, o.a];
+              result.predict = o.predict;
+              result.rSquared = o.rSquared;
+              return result;
+          }
+          if (order === 2) {
+              var o = quadratic().x(x).y(y).domain(domain)(data);
+              var result = [o[0], o[1]];
+              result.coefficients = [o.c, o.b, o.a];
+              result.predict = o.predict;
+              result.rSquared = o.rSquared;
+              return result;
+          }
+          var _a = points(data, x, y), xv = _a[0], yv = _a[1], ux = _a[2], uy = _a[3];
+          var n = xv.length;
+          var k = order + 1;
+          var lhs = [];
+          var rhs = [];
+          var Y = 0, n0 = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+          visitPoints(data, x, y, function (dx, dy) {
+              n0++;
+              Y += (dy - Y) / n0;
+              if (!domain) {
+                  if (dx < xmin)
+                      xmin = dx;
+                  if (dx > xmax)
+                      xmax = dx;
+              }
+          });
+          // Build normal equations
+          for (var i = 0; i < k; i++) {
+              // LHS
+              var v = 0;
+              for (var l = 0; l < n; l++) {
+                  v += Math.pow(xv[l], i) * yv[l];
+              }
+              lhs.push(v);
+              // RHS
+              var c = new Float64Array(k);
+              for (var j = 0; j < k; j++) {
+                  var v2 = 0;
+                  for (var l = 0; l < n; l++) {
+                      v2 += Math.pow(xv[l], i + j);
+                  }
+                  c[j] = v2;
+              }
+              rhs.push(c);
+          }
+          rhs.push(new Float64Array(lhs));
+          var coef = gaussianElimination(rhs);
+          var fn = function (xx) {
+              var shifted = xx - ux;
+              var val = uy + coef[0];
+              for (var i = 1; i < k; i++) {
+                  val += coef[i] * Math.pow(shifted, i);
+              }
+              return val;
+          };
+          var out = interpose(xmin, xmax, fn);
+          out.coefficients = uncenter(k, coef, -ux, uy);
+          out.predict = fn;
+          out.rSquared = determination(data, x, y, Y, fn);
+          return out;
+      };
+      polynomialRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return polynomialRegression;
+      };
+      polynomialRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return polynomialRegression;
+      };
+      polynomialRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return polynomialRegression;
+      };
+      polynomialRegression.order = function (n) {
+          if (!arguments.length)
+              return order;
+          order = n;
+          return polynomialRegression;
+      };
+      return polynomialRegression;
+  }
   function uncenter(k, a, x, y) {
-    var z = Array(k);
-    var i, j, v, c; // initialize to zero
-
-    for (i = 0; i < k; ++i) {
-      z[i] = 0;
-    } // polynomial expansion
-
-
-    for (i = k - 1; i >= 0; --i) {
-      v = a[i];
-      c = 1;
-      z[i] += v;
-
-      for (j = 1; j <= i; ++j) {
-        c *= (i + 1 - j) / j; // binomial coefficent
-
-        z[i - j] += v * Math.pow(x, j) * c;
+      var z = new Array(k).fill(0);
+      for (var i = k - 1; i >= 0; --i) {
+          var v = a[i];
+          z[i] += v;
+          var c = 1;
+          for (var j = 1; j <= i; ++j) {
+              c *= (i + 1 - j) / j; // binomial coefficient
+              z[i - j] += v * Math.pow(x, j) * c;
+          }
       }
-    } // bias term
-
-
-    z[0] += y;
-    return z;
-  } // Given an array for a two-dimensional matrix and the polynomial order,
-  // solve A * x = b using Gaussian elimination.
-
-
+      // bias term
+      z[0] += y;
+      return z;
+  }
+  // Solve A * x = b using Gaussian elimination
   function gaussianElimination(matrix) {
-    var n = matrix.length - 1,
-        coef = [];
-    var i, j, k, r, t;
-
-    for (i = 0; i < n; ++i) {
-      r = i; // max row
-
-      for (j = i + 1; j < n; ++j) {
-        if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
-          r = j;
-        }
-      }
-
-      for (k = i; k < n + 1; ++k) {
-        t = matrix[k][i];
-        matrix[k][i] = matrix[k][r];
-        matrix[k][r] = t;
-      }
-
-      for (j = i + 1; j < n; ++j) {
-        for (k = n; k >= i; k--) {
-          matrix[k][j] -= matrix[k][i] * matrix[i][j] / matrix[i][i];
-        }
+      var n = matrix.length - 1;
+      var coef = new Array(n);
+      for (var i = 0; i < n; i++) {
+          var r = i;
+          // find pivot row
+          for (var j = i + 1; j < n; j++) {
+              if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
+                  r = j;
+              }
+          }
+          // swap columns
+          for (var k = i; k < n + 1; k++) {
+              var t = matrix[k][i];
+              matrix[k][i] = matrix[k][r];
+              matrix[k][r] = t;
+          }
+          // reduce
+          for (var j = i + 1; j < n; j++) {
+              for (var k = n; k >= i; k--) {
+                  matrix[k][j] -= (matrix[k][i] * matrix[i][j]) / matrix[i][i];
+              }
+          }
       }
-    }
-
-    for (j = n - 1; j >= 0; --j) {
-      t = 0;
-
-      for (k = j + 1; k < n; ++k) {
-        t += matrix[k][j] * coef[k];
+      for (var j = n - 1; j >= 0; j--) {
+          var t = 0;
+          for (var k = j + 1; k < n; k++) {
+              t += matrix[k][j] * coef[k];
+          }
+          coef[j] = (matrix[n][j] - t) / matrix[j][j];
       }
-
-      coef[j] = (matrix[n][j] - t) / matrix[j][j];
-    }
-
-    return coef;
+      return coef;
   }
 
-  function power () {
-    var x = function x(d) {
-      return d[0];
-    },
-        y = function y(d) {
-      return d[1];
-    },
-        domain;
-
-    function power(data) {
-      var n = 0,
-          X = 0,
-          Y = 0,
-          XY = 0,
-          X2 = 0,
-          YS = 0,
-          xmin = domain ? +domain[0] : Infinity,
-          xmax = domain ? +domain[1] : -Infinity;
-      visitPoints(data, x, y, function (dx, dy) {
-        var lx = Math.log(dx),
-            ly = Math.log(dy);
-        ++n;
-        X += (lx - X) / n;
-        Y += (ly - Y) / n;
-        XY += (lx * ly - XY) / n;
-        X2 += (lx * lx - X2) / n;
-        YS += (dy - YS) / n;
-
-        if (!domain) {
-          if (dx < xmin) xmin = dx;
-          if (dx > xmax) xmax = dx;
-        }
-      });
-
-      var _ols = ols(X, Y, XY, X2),
-          _ols2 = _slicedToArray(_ols, 2),
-          a = _ols2[0],
-          b = _ols2[1];
-
-      a = Math.exp(a);
-
-      var fn = function fn(x) {
-        return a * Math.pow(x, b);
-      },
-          out = interpose(xmin, xmax, fn);
-
-      out.a = a;
-      out.b = b;
-      out.predict = fn;
-      out.rSquared = determination(data, x, y, YS, fn);
-      return out;
-    }
-
-    power.domain = function (arr) {
-      return arguments.length ? (domain = arr, power) : domain;
-    };
-
-    power.x = function (fn) {
-      return arguments.length ? (x = fn, power) : x;
-    };
-
-    power.y = function (fn) {
-      return arguments.length ? (y = fn, power) : y;
-    };
+  function power() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain;
+      var powerRegression = function powerRegression(data) {
+          var n = 0, X = 0, Y = 0, XY = 0, X2 = 0, YS = 0, xmin = domain ? +domain[0] : Infinity, xmax = domain ? +domain[1] : -Infinity;
+          visitPoints(data, x, y, function (dx, dy) {
+              var lx = Math.log(dx), ly = Math.log(dy);
+              ++n;
+              X += (lx - X) / n;
+              Y += (ly - Y) / n;
+              XY += (lx * ly - XY) / n;
+              X2 += (lx * lx - X2) / n;
+              YS += (dy - YS) / n;
+              if (!domain) {
+                  if (dx < xmin)
+                      xmin = dx;
+                  if (dx > xmax)
+                      xmax = dx;
+              }
+          });
+          var _a = ols(X, Y, XY, X2), a = _a[0], b = _a[1];
+          a = Math.exp(a);
+          var fn = function (xx) { return a * Math.pow(xx, b); };
+          var out = interpose(xmin, xmax, fn);
+          out.a = a;
+          out.b = b;
+          out.predict = fn;
+          out.rSquared = determination(data, x, y, YS, fn);
+          return out;
+      };
+      powerRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return powerRegression;
+      };
+      powerRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return powerRegression;
+      };
+      powerRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return powerRegression;
+      };
+      return powerRegression;
+  }
 
-    return power;
+  function sigmoidal() {
+      var x = function (d) { return d[0]; }, y = function (d) { return d[1]; }, domain, maxIter = 2000, alpha = 1e-4; // learning rate
+      function sigmoidalRegression(data) {
+          var n = 0, Xmin = domain ? +domain[0] : Infinity, Xmax = domain ? +domain[1] : -Infinity, Ymin = Infinity, Ymax = -Infinity, sumY = 0;
+          // Gather data & track min/max for domain if not preset
+          visitPoints(data, x, y, function (dx, dy) {
+              n++;
+              sumY += dy;
+              if (!domain) {
+                  if (dx < Xmin)
+                      Xmin = dx;
+                  if (dx > Xmax)
+                      Xmax = dx;
+              }
+              if (dy < Ymin)
+                  Ymin = dy;
+              if (dy > Ymax)
+                  Ymax = dy;
+          });
+          if (!domain && (Xmin === Infinity || Xmax === -Infinity)) {
+              Xmin = 0;
+              Xmax = 1;
+          }
+          // Initialize parameters
+          var A = Ymax - Ymin; // amplitude
+          var B = 1; // logistic slope
+          var C = Ymin; // baseline offset
+          var M = (Xmin + Xmax) / 2; // midpoint
+          // Predict function
+          function f(xx) {
+              return C + A / (1 + Math.exp(-B * (xx - M)));
+          }
+          var _loop_1 = function (iter) {
+              var dA = 0, dB = 0, dC = 0, dM = 0;
+              visitPoints(data, x, y, function (dx, dy) {
+                  var yhat = f(dx);
+                  var err = dy - yhat;
+                  var ex = Math.exp(-B * (dx - M));
+                  var g = 1 / (1 + ex); // logistic
+                  // partial derivatives
+                  var df_dA = g;
+                  var df_dC = 1;
+                  var df_dB = A * g * (1 - g) * (dx - M);
+                  // Note the negative sign for M:
+                  var df_dM = -A * B * g * (1 - g);
+                  var factor = -2 * err;
+                  dA += factor * df_dA;
+                  dB += factor * df_dB;
+                  dC += factor * df_dC;
+                  dM += factor * df_dM;
+              });
+              A -= alpha * (dA / n);
+              B -= alpha * (dB / n);
+              C -= alpha * (dC / n);
+              M -= alpha * (dM / n);
+          };
+          // Gradient Descent
+          for (var iter = 0; iter < maxIter; iter++) {
+              _loop_1();
+          }
+          var predict = function (xx) { return C + A / (1 + Math.exp(-B * (xx - M))); };
+          var out = interpose(Xmin, Xmax, predict);
+          out.A = A;
+          out.B = B;
+          out.C = C;
+          out.M = M;
+          out.predict = predict;
+          // R^2
+          var meanY = sumY / n;
+          out.rSquared = determination(data, x, y, meanY, predict);
+          return out;
+      }
+      sigmoidalRegression.domain = function (arr) {
+          if (!arguments.length)
+              return domain;
+          domain = arr;
+          return sigmoidalRegression;
+      };
+      sigmoidalRegression.x = function (fn) {
+          if (!arguments.length)
+              return x;
+          x = fn;
+          return sigmoidalRegression;
+      };
+      sigmoidalRegression.y = function (fn) {
+          if (!arguments.length)
+              return y;
+          y = fn;
+          return sigmoidalRegression;
+      };
+      return sigmoidalRegression;
   }
 
   exports.regressionExp = exponential;
@@ -878,7 +769,8 @@
   exports.regressionLog = logarithmic;
   exports.regressionPoly = polynomial;
   exports.regressionPow = power;
-  exports.regressionQuad = quad;
+  exports.regressionQuad = quadratic;
+  exports.regressionSigmoidal = sigmoidal;
 
   Object.defineProperty(exports, '__esModule', { value: true });
 
diff --git a/dist/d3-regression.min.js b/dist/d3-regression.min.js
index 1a93fd8..1227620 100644
--- a/dist/d3-regression.min.js
+++ b/dist/d3-regression.min.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/dist/d3-regression.zip b/dist/d3-regression.zip
deleted file mode 100644
index f9a108d..0000000
Binary files a/dist/d3-regression.zip and /dev/null differ
diff --git a/dist/index.d.ts b/dist/index.d.ts
new file mode 100644
index 0000000..36e93bf
--- /dev/null
+++ b/dist/index.d.ts
@@ -0,0 +1,8 @@
+export { default as regressionExp } from "./src/exponential";
+export { default as regressionLinear } from "./src/linear";
+export { default as regressionLoess } from "./src/loess";
+export { default as regressionLog } from "./src/logarithmic";
+export { default as regressionPoly } from "./src/polynomial";
+export { default as regressionPow } from "./src/power";
+export { default as regressionQuad } from "./src/quadratic";
+export { default as regressionSigmoidal } from "./src/sigmoidal";
diff --git a/dist/src/exponential.d.ts b/dist/src/exponential.d.ts
new file mode 100644
index 0000000..8ff11b9
--- /dev/null
+++ b/dist/src/exponential.d.ts
@@ -0,0 +1,17 @@
+import { PredictFunction, Domain, DataPoint, Accessor } from "./types";
+export type ExponentialOutput = [DataPoint, DataPoint] & {
+    a: number;
+    b: number;
+    predict: PredictFunction;
+    rSquared: number;
+};
+export interface ExponentialRegression<T> {
+    (data: T[]): ExponentialOutput;
+    domain(): Domain;
+    domain(arr: Domain): this;
+    x(): Accessor<T>;
+    x(fn: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(fn: Accessor<T>): this;
+}
+export default function exponential<T = DataPoint>(): ExponentialRegression<T>;
diff --git a/dist/src/linear.d.ts b/dist/src/linear.d.ts
new file mode 100644
index 0000000..7857536
--- /dev/null
+++ b/dist/src/linear.d.ts
@@ -0,0 +1,17 @@
+import { PredictFunction, Accessor, DataPoint, Domain } from "./types";
+export type LinearOutput = [DataPoint, DataPoint] & {
+    a: number;
+    b: number;
+    predict: PredictFunction;
+    rSquared: number;
+};
+export interface LinearRegression<T> {
+    (data: T[]): LinearOutput;
+    domain(): Domain;
+    domain(arr: Domain): this;
+    x(): Accessor<T>;
+    x(fn: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(fn: Accessor<T>): this;
+}
+export default function linear<T = DataPoint>(): LinearRegression<T>;
diff --git a/dist/src/loess.d.ts b/dist/src/loess.d.ts
new file mode 100644
index 0000000..ec2f0e6
--- /dev/null
+++ b/dist/src/loess.d.ts
@@ -0,0 +1,11 @@
+import { Accessor, DataPoint } from "./types";
+export interface LoessRegression<T> {
+    (data: T[]): DataPoint[];
+    bandwidth(): number;
+    bandwidth(bw: number): this;
+    x(): Accessor<T>;
+    x(fn: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(fn: Accessor<T>): this;
+}
+export default function loess<T = DataPoint>(): LoessRegression<T>;
diff --git a/dist/src/logarithmic.d.ts b/dist/src/logarithmic.d.ts
new file mode 100644
index 0000000..1420628
--- /dev/null
+++ b/dist/src/logarithmic.d.ts
@@ -0,0 +1,20 @@
+import { PredictFunction, DataPoint, Accessor, Domain } from "./types";
+type LogarithmicOutput = [DataPoint, DataPoint] & {
+    a: number;
+    b: number;
+    predict: PredictFunction;
+    rSquared: number;
+};
+export interface LogarithmicRegression<T> {
+    (data: T[]): LogarithmicOutput;
+    domain(): Domain;
+    domain(arr: Domain): this;
+    x(): Accessor<T>;
+    x(fn: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(fn: Accessor<T>): this;
+    base(): number;
+    base(b: number): this;
+}
+export default function logarithmic<T = DataPoint>(): LogarithmicRegression<T>;
+export {};
diff --git a/dist/src/polynomial.d.ts b/dist/src/polynomial.d.ts
new file mode 100644
index 0000000..b32400a
--- /dev/null
+++ b/dist/src/polynomial.d.ts
@@ -0,0 +1,18 @@
+import { PredictFunction, Accessor, DataPoint, Domain } from "./types";
+export type PolynomialOutput = [DataPoint, DataPoint] & {
+    coefficients: number[];
+    predict: PredictFunction;
+    rSquared: number;
+};
+export interface PolynomialRegression<T> {
+    (data: T[]): PolynomialOutput;
+    domain(): Domain;
+    domain(domain?: Domain): this;
+    x(): Accessor<T>;
+    x(x: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(y: Accessor<T>): this;
+    order(): number;
+    order(order: number): this;
+}
+export default function polynomial<T = DataPoint>(): PolynomialRegression<T>;
diff --git a/dist/src/power.d.ts b/dist/src/power.d.ts
new file mode 100644
index 0000000..c6b8f9a
--- /dev/null
+++ b/dist/src/power.d.ts
@@ -0,0 +1,18 @@
+import { PredictFunction, DataPoint, Accessor, Domain } from "./types";
+export type PowerOutput = [DataPoint, DataPoint] & {
+    a: number;
+    b: number;
+    predict: PredictFunction;
+    rSquared: number;
+};
+interface PowerRegression<T> {
+    (data: T[]): PowerOutput;
+    domain(): Domain;
+    domain(domain?: Domain): this;
+    x(): Accessor<T>;
+    x(x: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(y: Accessor<T>): this;
+}
+export default function power<T = DataPoint>(): PowerRegression<T>;
+export {};
diff --git a/dist/src/quadratic.d.ts b/dist/src/quadratic.d.ts
new file mode 100644
index 0000000..15c6567
--- /dev/null
+++ b/dist/src/quadratic.d.ts
@@ -0,0 +1,19 @@
+import { Accessor, DataPoint, PredictFunction, Domain } from "./types";
+export type QuadraticOutput = [DataPoint, DataPoint] & {
+    a: number;
+    b: number;
+    c: number;
+    predict: PredictFunction;
+    rSquared: number;
+};
+interface QuadraticRegression<T> {
+    (data: T[]): QuadraticOutput;
+    domain(): Domain;
+    domain(domain?: Domain): this;
+    x(): Accessor<T>;
+    x(x: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(y: Accessor<T>): this;
+}
+export default function quadratic<T = DataPoint>(): QuadraticRegression<T>;
+export {};
diff --git a/dist/src/sigmoidal.d.ts b/dist/src/sigmoidal.d.ts
new file mode 100644
index 0000000..b7ffe6f
--- /dev/null
+++ b/dist/src/sigmoidal.d.ts
@@ -0,0 +1,24 @@
+import { PredictFunction, Domain, DataPoint, Accessor } from "./types";
+/**
+ * Sigmoidal (logistic) regression of the form:
+ *   f(x) = C + A / (1 + exp(-B * (x - M)))
+ * where A, B, C, M are parameters found via gradient descent.
+ */
+export type SigmoidalOutput = [DataPoint, DataPoint] & {
+    A: number;
+    B: number;
+    C: number;
+    M: number;
+    predict: PredictFunction;
+    rSquared: number;
+};
+export interface SigmoidalRegression<T> {
+    (data: T[]): SigmoidalOutput;
+    domain(): Domain;
+    domain(domain: Domain): this;
+    x(): Accessor<T>;
+    x(fn: Accessor<T>): this;
+    y(): Accessor<T>;
+    y(fn: Accessor<T>): this;
+}
+export default function sigmoidal<T = DataPoint>(): SigmoidalRegression<T>;
diff --git a/dist/src/types.d.ts b/dist/src/types.d.ts
new file mode 100644
index 0000000..f31848d
--- /dev/null
+++ b/dist/src/types.d.ts
@@ -0,0 +1,4 @@
+export type DataPoint = [number, number];
+export type Accessor<T> = (d: T, i?: number, data?: T[]) => number;
+export type PredictFunction = (x: number) => number;
+export type Domain = [number, number] | undefined;
diff --git a/dist/src/utils/determination.d.ts b/dist/src/utils/determination.d.ts
new file mode 100644
index 0000000..69bbed2
--- /dev/null
+++ b/dist/src/utils/determination.d.ts
@@ -0,0 +1,6 @@
+import { Accessor, PredictFunction } from "../types";
+/**
+ * Given a dataset, x- and y-accessors, the mean center of the y-values (uY),
+ * and a prediction function, return the coefficient of determination, R^2.
+ */
+export declare function determination<T>(data: T[], x: Accessor<T>, y: Accessor<T>, uY: number, predict: PredictFunction): number;
diff --git a/dist/src/utils/geometry.d.ts b/dist/src/utils/geometry.d.ts
new file mode 100644
index 0000000..b6f871f
--- /dev/null
+++ b/dist/src/utils/geometry.d.ts
@@ -0,0 +1,9 @@
+import { DataPoint } from "../types";
+/**
+ * Returns the angle of a line in degrees.
+ */
+export declare function angle(line: [DataPoint, DataPoint]): number;
+/**
+ * Returns the midpoint of a line.
+ */
+export declare function midpoint(line: [DataPoint, DataPoint]): DataPoint;
diff --git a/dist/src/utils/interpose.d.ts b/dist/src/utils/interpose.d.ts
new file mode 100644
index 0000000..1c5dd07
--- /dev/null
+++ b/dist/src/utils/interpose.d.ts
@@ -0,0 +1,6 @@
+import { PredictFunction, DataPoint } from "../types";
+/**
+ * Given a start point (xmin), an end point (xmax),
+ * and a prediction function, returns a smooth line.
+ */
+export declare function interpose(xmin: number, xmax: number, predict: PredictFunction): [DataPoint, DataPoint];
diff --git a/dist/src/utils/median.d.ts b/dist/src/utils/median.d.ts
new file mode 100644
index 0000000..0022487
--- /dev/null
+++ b/dist/src/utils/median.d.ts
@@ -0,0 +1,4 @@
+/**
+ * Returns the median value of an array of numbers.
+ */
+export declare function median(arr: Float64Array): number;
diff --git a/dist/src/utils/ols.d.ts b/dist/src/utils/ols.d.ts
new file mode 100644
index 0000000..fbca901
--- /dev/null
+++ b/dist/src/utils/ols.d.ts
@@ -0,0 +1,6 @@
+/**
+ * Ordinary Least Squares from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+ */
+export declare function ols(uX: number, uY: number, uXY: number, uX2: number): [number, number];
diff --git a/dist/src/utils/points.d.ts b/dist/src/utils/points.d.ts
new file mode 100644
index 0000000..f471c45
--- /dev/null
+++ b/dist/src/utils/points.d.ts
@@ -0,0 +1,11 @@
+import { Accessor } from "../types";
+/**
+ * Adapted from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+ */
+export declare function points<T>(data: T[], x: Accessor<T>, y: Accessor<T>, sort?: boolean): [Float64Array, Float64Array, number, number];
+/**
+ * Iterates over valid data points, invoking a callback for each.
+ */
+export declare function visitPoints<T>(data: T[], x: Accessor<T>, y: Accessor<T>, cb: (dx: number, dy: number, index: number) => void): void;
diff --git a/index.js b/index.ts
similarity index 73%
rename from index.js
rename to index.ts
index 3159d4e..d90ba6e 100644
--- a/index.js
+++ b/index.ts
@@ -4,4 +4,5 @@ export {default as regressionLoess} from "./src/loess";
 export {default as regressionLog} from "./src/logarithmic";
 export {default as regressionPoly} from "./src/polynomial";
 export {default as regressionPow} from "./src/power";
-export {default as regressionQuad} from "./src/quadratic";
\ No newline at end of file
+export {default as regressionQuad} from "./src/quadratic";
+export {default as regressionSigmoidal} from "./src/sigmoidal";
diff --git a/package-lock.json b/package-lock.json
index cb1315d..3a163ba 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,24 +1,44 @@
 {
   "name": "d3-regression",
   "version": "1.3.10",
-  "lockfileVersion": 1,
+  "lockfileVersion": 3,
   "requires": true,
-  "dependencies": {
-    "@babel/code-frame": {
+  "packages": {
+    "": {
+      "name": "d3-regression",
+      "version": "1.3.10",
+      "license": "BSD-3-Clause",
+      "devDependencies": {
+        "@babel/core": "^7.4.4",
+        "@babel/plugin-transform-block-scoping": "^7.25.9",
+        "@babel/preset-env": "^7.4.4",
+        "babel": "^6.23.0",
+        "package-preamble": "^0.1.0",
+        "rollup": "^2.10.0",
+        "rollup-plugin-babel": "^4.4.0",
+        "rollup-plugin-commonjs": "^10.1.0",
+        "rollup-plugin-node-resolve": "^5.2.0",
+        "rollup-plugin-typescript2": "^0.35.0",
+        "tape": "^4.10.1",
+        "typescript": "^4.9.5",
+        "uglify-js": "^2.8.29"
+      }
+    },
+    "node_modules/@babel/code-frame": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.0.0.tgz",
       "integrity": "sha512-OfC2uemaknXr87bdLUkWog7nYuliM9Ij5HUcajsVcMCpQrcLmtxRbVFTIqmcSkSeYRBFBRxs2FiUqFJDLdiebA==",
       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/highlight": "^7.0.0"
       }
     },
-    "@babel/core": {
+    "node_modules/@babel/core": {
       "version": "7.4.4",
       "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.4.4.tgz",
       "integrity": "sha512-lQgGX3FPRgbz2SKmhMtYgJvVzGZrmjaF4apZ2bLwofAKiSjxU0drPh4S/VasyYXwaTs+A1gvQ45BN8SQJzHsQQ==",
       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/code-frame": "^7.0.0",
         "@babel/generator": "^7.4.4",
         "@babel/helpers": "^7.4.4",
@@ -34,93 +54,100 @@
         "semver": "^5.4.1",
         "source-map": "^0.5.0"
       },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/core/node_modules/@babel/generator": {
+      "version": "7.4.4",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.4.4.tgz",
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+      "dev": true,
+      "dependencies": {
+        "@babel/types": "^7.4.4",
+        "jsesc": "^2.5.1",
+        "lodash": "^4.17.11",
+        "source-map": "^0.5.0",
+        "trim-right": "^1.0.1"
+      }
+    },
+    "node_modules/@babel/core/node_modules/@babel/helper-split-export-declaration": {
+      "version": "7.4.4",
+      "resolved": "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.4.4.tgz",
+      "integrity": "sha512-Ro/XkzLf3JFITkW6b+hNxzZ1n5OQ80NvIUdmHspih1XAhtN3vPTuUFT4eQnela+2MaZ5ulH+iyP513KJrxbN7Q==",
+      "dev": true,
+      "dependencies": {
+        "@babel/types": "^7.4.4"
+      }
+    },
+    "node_modules/@babel/core/node_modules/@babel/helpers": {
+      "version": "7.4.4",
+      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.4.4.tgz",
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+      "dev": true,
+      "dependencies": {
+        "@babel/template": "^7.4.4",
+        "@babel/traverse": "^7.4.4",
+        "@babel/types": "^7.4.4"
+      }
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+    "node_modules/@babel/core/node_modules/@babel/parser": {
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+      "dev": true,
+      "bin": {
+        "parser": "bin/babel-parser.js"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@babel/core/node_modules/@babel/template": {
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+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.4.4.tgz",
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+      }
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+      "version": "7.4.4",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.4.4.tgz",
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+      "dev": true,
+      "dependencies": {
+        "@babel/code-frame": "^7.0.0",
+        "@babel/generator": "^7.4.4",
+        "@babel/helper-function-name": "^7.1.0",
+        "@babel/helper-split-export-declaration": "^7.4.4",
+        "@babel/parser": "^7.4.4",
+        "@babel/types": "^7.4.4",
+        "debug": "^4.1.0",
+        "globals": "^11.1.0",
+        "lodash": "^4.17.11"
+      }
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-          "dev": true,
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-            "@babel/generator": "^7.4.4",
-            "@babel/helper-function-name": "^7.1.0",
-            "@babel/helper-split-export-declaration": "^7.4.4",
-            "@babel/parser": "^7.4.4",
-            "@babel/types": "^7.4.4",
-            "debug": "^4.1.0",
-            "globals": "^11.1.0",
-            "lodash": "^4.17.11"
-          }
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-        "@babel/types": {
-          "version": "7.4.4",
-          "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.4.4.tgz",
-          "integrity": "sha512-dOllgYdnEFOebhkKCjzSVFqw/PmmB8pH6RGOWkY4GsboQNd47b1fBThBSwlHAq9alF9vc1M3+6oqR47R50L0tQ==",
-          "dev": true,
-          "requires": {
-            "esutils": "^2.0.2",
-            "lodash": "^4.17.11",
-            "to-fast-properties": "^2.0.0"
-          }
-        }
-      }
-    },
-    "@babel/generator": {
+        "esutils": "^2.0.2",
+        "lodash": "^4.17.11",
+        "to-fast-properties": "^2.0.0"
+      }
+    },
+    "node_modules/@babel/generator": {
       "version": "7.4.0",
       "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.4.0.tgz",
       "integrity": "sha512-/v5I+a1jhGSKLgZDcmAUZ4K/VePi43eRkUs3yePW1HB1iANOD5tqJXwGSG4BZhSksP8J9ejSlwGeTiiOFZOrXQ==",
       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/types": "^7.4.0",
         "jsesc": "^2.5.1",
         "lodash": "^4.17.11",
@@ -128,79 +155,79 @@
         "trim-right": "^1.0.1"
       }
     },
-    "@babel/helper-annotate-as-pure": {
+    "node_modules/@babel/helper-annotate-as-pure": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.0.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/types": "^7.0.0"
       }
     },
-    "@babel/helper-builder-binary-assignment-operator-visitor": {
+    "node_modules/@babel/helper-builder-binary-assignment-operator-visitor": {
       "version": "7.1.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-builder-binary-assignment-operator-visitor/-/helper-builder-binary-assignment-operator-visitor-7.1.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/helper-explode-assignable-expression": "^7.1.0",
         "@babel/types": "^7.0.0"
       }
     },
-    "@babel/helper-explode-assignable-expression": {
+    "node_modules/@babel/helper-explode-assignable-expression": {
       "version": "7.1.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-explode-assignable-expression/-/helper-explode-assignable-expression-7.1.0.tgz",
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       }
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-    "@babel/helper-function-name": {
+    "node_modules/@babel/helper-function-name": {
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       "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.1.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/helper-get-function-arity": "^7.0.0",
         "@babel/template": "^7.1.0",
         "@babel/types": "^7.0.0"
       }
     },
-    "@babel/helper-get-function-arity": {
+    "node_modules/@babel/helper-get-function-arity": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-get-function-arity/-/helper-get-function-arity-7.0.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/types": "^7.0.0"
       }
     },
-    "@babel/helper-member-expression-to-functions": {
+    "node_modules/@babel/helper-member-expression-to-functions": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.0.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/types": "^7.0.0"
       }
     },
-    "@babel/helper-module-imports": {
+    "node_modules/@babel/helper-module-imports": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.0.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/types": "^7.0.0"
       }
     },
-    "@babel/helper-module-transforms": {
+    "node_modules/@babel/helper-module-transforms": {
       "version": "7.2.2",
       "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.2.2.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "@babel/helper-module-imports": "^7.0.0",
         "@babel/helper-simple-access": "^7.1.0",
         "@babel/helper-split-export-declaration": "^7.0.0",
@@ -209,36 +236,40 @@
         "lodash": "^4.17.10"
       }
     },
-    "@babel/helper-optimise-call-expression": {
+    "node_modules/@babel/helper-optimise-call-expression": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-optimise-call-expression/-/helper-optimise-call-expression-7.0.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
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       }
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-    "@babel/helper-plugin-utils": {
-      "version": "7.0.0",
-      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.0.0.tgz",
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-      "dev": true
+    "node_modules/@babel/helper-plugin-utils": {
+      "version": "7.26.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.26.5.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
     },
-    "@babel/helper-regex": {
+    "node_modules/@babel/helper-regex": {
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-regex/-/helper-regex-7.0.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
         "lodash": "^4.17.10"
       }
     },
-    "@babel/helper-remap-async-to-generator": {
+    "node_modules/@babel/helper-remap-async-to-generator": {
       "version": "7.1.0",
       "resolved": "https://registry.npmjs.org/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.1.0.tgz",
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       "dev": true,
-      "requires": {
+      "dependencies": {
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         "@babel/helper-wrap-function": "^7.1.0",
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@@ -246,298 +277,396 @@
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-      "dependencies": {
-        "minimist": {
-          "version": "1.2.0",
-          "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
-          "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
-          "dev": true
-        }
+      "bin": {
+        "tape": "bin/tape"
       }
     },
-    "through": {
+    "node_modules/tape/node_modules/minimist": {
+      "version": "1.2.0",
+      "resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
+      "integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
+      "dev": true
+    },
+    "node_modules/through": {
       "version": "2.3.8",
       "resolved": "https://registry.npmjs.org/through/-/through-2.3.8.tgz",
       "integrity": "sha1-DdTJ/6q8NXlgsbckEV1+Doai4fU=",
       "dev": true
     },
-    "to-fast-properties": {
+    "node_modules/to-fast-properties": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/to-fast-properties/-/to-fast-properties-2.0.0.tgz",
       "integrity": "sha1-3F5pjL0HkmW8c+A3doGk5Og/YW4=",
-      "dev": true
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
     },
-    "trim-right": {
+    "node_modules/trim-right": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/trim-right/-/trim-right-1.0.1.tgz",
       "integrity": "sha1-yy4SAwZ+DI3h9hQJS5/kVwTqYAM=",
-      "dev": true
+      "dev": true,
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/tslib": {
+      "version": "2.8.1",
+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
+      "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
+      "dev": true,
+      "license": "0BSD"
+    },
+    "node_modules/typescript": {
+      "version": "4.9.5",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.9.5.tgz",
+      "integrity": "sha512-1FXk9E2Hm+QzZQ7z+McJiHL4NW1F2EzMu9Nq9i3zAaGqibafqYwCVU6WyWAuyQRRzOlxou8xZSyXLEN8oKj24g==",
+      "dev": true,
+      "license": "Apache-2.0",
+      "bin": {
+        "tsc": "bin/tsc",
+        "tsserver": "bin/tsserver"
+      },
+      "engines": {
+        "node": ">=4.2.0"
+      }
     },
-    "uglify-js": {
+    "node_modules/uglify-js": {
       "version": "2.8.29",
       "resolved": "https://registry.npmjs.org/uglify-js/-/uglify-js-2.8.29.tgz",
       "integrity": "sha1-KcVzMUgFe7Th913zW3qcty5qWd0=",
       "dev": true,
-      "requires": {
+      "dependencies": {
         "source-map": "~0.5.1",
-        "uglify-to-browserify": "~1.0.0",
         "yargs": "~3.10.0"
+      },
+      "bin": {
+        "uglifyjs": "bin/uglifyjs"
+      },
+      "engines": {
+        "node": ">=0.8.0"
+      },
+      "optionalDependencies": {
+        "uglify-to-browserify": "~1.0.0"
       }
     },
-    "uglify-to-browserify": {
+    "node_modules/uglify-to-browserify": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/uglify-to-browserify/-/uglify-to-browserify-1.0.2.tgz",
       "integrity": "sha1-bgkk1r2mta/jSeOabWMoUKD4grc=",
       "dev": true,
       "optional": true
     },
-    "unicode-canonical-property-names-ecmascript": {
+    "node_modules/unicode-canonical-property-names-ecmascript": {
       "version": "1.0.4",
       "resolved": "https://registry.npmjs.org/unicode-canonical-property-names-ecmascript/-/unicode-canonical-property-names-ecmascript-1.0.4.tgz",
       "integrity": "sha512-jDrNnXWHd4oHiTZnx/ZG7gtUTVp+gCcTTKr8L0HjlwphROEW3+Him+IpvC+xcJEFegapiMZyZe02CyuOnRmbnQ==",
-      "dev": true
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
     },
-    "unicode-match-property-ecmascript": {
+    "node_modules/unicode-match-property-ecmascript": {
       "version": "1.0.4",
       "resolved": "https://registry.npmjs.org/unicode-match-property-ecmascript/-/unicode-match-property-ecmascript-1.0.4.tgz",
       "integrity": "sha512-L4Qoh15vTfntsn4P1zqnHulG0LdXgjSO035fEpdtp6YxXhMT51Q6vgM5lYdG/5X3MjS+k/Y9Xw4SFCY9IkR0rg==",
       "dev": true,
-      "requires": {
+      "dependencies": {
         "unicode-canonical-property-names-ecmascript": "^1.0.4",
         "unicode-property-aliases-ecmascript": "^1.0.4"
+      },
+      "engines": {
+        "node": ">=4"
       }
     },
-    "unicode-match-property-value-ecmascript": {
+    "node_modules/unicode-match-property-value-ecmascript": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/unicode-match-property-value-ecmascript/-/unicode-match-property-value-ecmascript-1.1.0.tgz",
       "integrity": "sha512-hDTHvaBk3RmFzvSl0UVrUmC3PuW9wKVnpoUDYH0JDkSIovzw+J5viQmeYHxVSBptubnr7PbH2e0fnpDRQnQl5g==",
-      "dev": true
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
     },
-    "unicode-property-aliases-ecmascript": {
+    "node_modules/unicode-property-aliases-ecmascript": {
       "version": "1.0.5",
       "resolved": "https://registry.npmjs.org/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-1.0.5.tgz",
       "integrity": "sha512-L5RAqCfXqAwR3RriF8pM0lU0w4Ryf/GgzONwi6KnL1taJQa7x1TCxdJnILX59WIGOwR57IVxn7Nej0fz1Ny6fw==",
-      "dev": true
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/universalify": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/universalify/-/universalify-2.0.1.tgz",
+      "integrity": "sha512-gptHNQghINnc/vTGIk0SOFGFNXw7JVrlRUtConJRlvaw6DuX0wO5Jeko9sWrMBhh+PsYAZ7oXAiOnf/UKogyiw==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">= 10.0.0"
+      }
     },
-    "window-size": {
+    "node_modules/window-size": {
       "version": "0.1.0",
       "resolved": "https://registry.npmjs.org/window-size/-/window-size-0.1.0.tgz",
       "integrity": "sha1-VDjNLqk7IC76Ohn+iIeu58lPnJ0=",
-      "dev": true
+      "dev": true,
+      "engines": {
+        "node": ">= 0.8.0"
+      }
     },
-    "wrappy": {
+    "node_modules/wrappy": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
       "integrity": "sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=",
       "dev": true
     },
-    "yargs": {
+    "node_modules/yargs": {
       "version": "3.10.0",
       "resolved": "https://registry.npmjs.org/yargs/-/yargs-3.10.0.tgz",
       "integrity": "sha1-9+572FfdfB0tOMDnTvvWgdFDH9E=",
       "dev": true,
-      "requires": {
+      "dependencies": {
         "camelcase": "^1.0.2",
         "cliui": "^2.1.0",
         "decamelize": "^1.0.0",
diff --git a/package.json b/package.json
index 26fd497..babb340 100644
--- a/package.json
+++ b/package.json
@@ -12,7 +12,7 @@
     "pretest": "rm -rf dist && mkdir dist && rollup -c --banner \"$(preamble)\"",
     "test": "tape 'test/**/*-test.js'",
     "prepublishOnly": "npm run test && uglifyjs dist/d3-regression.js -c -m -o dist/d3-regression.min.js",
-    "postpublish": "zip -j dist/d3-regression.zip -- LICENSE README.md dist/d3-regression.js dist/d3-regression.min.js dist/d3-regression.cjs.js dist/d3-regression.esm.js"
+    "postpublish": "zip -j dist/d3-regression.zip -- LICENSE README.md dist/d3-regression.js dist/d3-regression.min.js dist/d3-regression.cjs.js dist/d3-regression.esm.js dist/index.d.ts dist/src/*.d.ts"
   },
   "repository": {
     "type": "git",
@@ -25,6 +25,7 @@
   "browser": "dist/d3-regression.js",
   "module": "dist/d3-regression.esm.js",
   "main": "dist/d3-regression.cjs.js",
+  "types": "dist/types/index.d.ts",
   "license": "BSD-3-Clause",
   "bugs": {
     "url": "https://github.com/HarryStevens/d3-regression/issues"
@@ -39,8 +40,9 @@
     "rollup-plugin-babel": "^4.4.0",
     "rollup-plugin-commonjs": "^10.1.0",
     "rollup-plugin-node-resolve": "^5.2.0",
+    "rollup-plugin-typescript2": "^0.35.0",
     "tape": "^4.10.1",
+    "typescript": "^4.9.5",
     "uglify-js": "^2.8.29"
-  },
-  "dependencies": {}
+  }
 }
diff --git a/rollup.config.js b/rollup.config.js
index b192098..17a58d4 100644
--- a/rollup.config.js
+++ b/rollup.config.js
@@ -2,11 +2,12 @@ import resolve from "rollup-plugin-node-resolve";
 import commonjs from "rollup-plugin-commonjs";
 import babel from "rollup-plugin-babel";
 import pkg from "./package.json";
+import typescript from "rollup-plugin-typescript2";
 
 export default [
   // browser-friendly UMD build
   {
-    input: "index.js",
+    input: "index.ts",
     output: {
       name: "d3",
       file: pkg.browser,
@@ -15,6 +16,16 @@ export default [
     plugins: [
       resolve(),
       commonjs(),
+      typescript({
+        tsconfig: "./tsconfig.json",
+        tsconfigOverride: {
+          compilerOptions: {
+            declaration: true,
+            declarationDir: "./dist/types",
+            allowJs: true
+          }
+        }
+      }),
       babel({
         exclude: ["node_modules/**"],
       }),
@@ -22,19 +33,24 @@ export default [
   },
 
   // CommonJS (for Node) and ES module (for bundlers) build.
-  // (We could have three entries in the configuration array
-  // instead of two, but it's quicker to generate multiple
-  // builds from a single configuration where possible, using
-  // an array for the `output` option, where we can specify
-  // `file` and `format` for each target)
   {
-    input: "index.js",
-    external: [], // list dependencies here
+    input: "index.ts",
+    external: [], // list dependencies here if needed
     output: [
       { file: pkg.main, format: "cjs" },
       { file: pkg.module, format: "es" },
     ],
     plugins: [
+      typescript({
+        tsconfig: "./tsconfig.json",
+        tsconfigOverride: {
+          compilerOptions: {
+            declaration: true,
+            declarationDir: "./dist/types",
+            allowJs: true
+          }
+        }
+      }),
       babel({
         exclude: ["node_modules/**"],
       }),
diff --git a/src/exponential.js b/src/exponential.js
deleted file mode 100644
index 3b33798..0000000
--- a/src/exponential.js
+++ /dev/null
@@ -1,62 +0,0 @@
-import { determination } from "./utils/determination";
-import { interpose } from "./utils/interpose";
-import { ols } from "./utils/ols";
-import { visitPoints } from "./utils/points";
-
-export default function() {
-  let x = d => d[0],
-      y = d => d[1],
-      domain;
-   
-  function exponential(data){
-    let n = 0,
-        Y = 0,
-        YL = 0,
-        XY = 0,
-        XYL = 0,
-        X2Y = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-
-    visitPoints(data, x, y, (dx, dy) => {
-      const ly = Math.log(dy), xy = dx * dy;
-      ++n;
-      Y += (dy - Y) / n;
-      XY += (xy - XY) / n;
-      X2Y += (dx * xy - X2Y) / n;
-      YL += (dy * ly - YL) / n;
-      XYL += (xy * ly - XYL) / n;
-      
-      if (!domain){
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-    
-    let [a, b] = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y);
-    a = Math.exp(a);
-    const fn = x => a * Math.exp(b * x),
-          out = interpose(xmin, xmax, fn);
-    
-    out.a = a;
-    out.b = b;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    
-    return out;  
-  }
-
-  exponential.domain = function(arr){
-    return arguments.length ? (domain = arr, exponential) : domain;
-  }  
-  
-  exponential.x = function(fn){
-    return arguments.length ? (x = fn, exponential) : x;
-  }
-
-  exponential.y = function(fn){
-    return arguments.length ? (y = fn, exponential) : y;
-  }
-  
-  return exponential;
-}
\ No newline at end of file
diff --git a/src/exponential.ts b/src/exponential.ts
new file mode 100644
index 0000000..dc1f310
--- /dev/null
+++ b/src/exponential.ts
@@ -0,0 +1,92 @@
+import { determination } from "./utils/determination";
+import { interpose } from "./utils/interpose";
+import { ols } from "./utils/ols";
+import { visitPoints } from "./utils/points";
+import { PredictFunction, Domain, DataPoint, Accessor } from "./types";
+
+
+export type ExponentialOutput = [DataPoint, DataPoint] & {
+  a: number;               // slope
+  b: number;               // intercept
+  predict: PredictFunction
+  rSquared: number;
+}
+
+export interface ExponentialRegression<T>  {
+  (data: T[]): ExponentialOutput;
+  
+  domain(): Domain;
+  domain(arr: Domain): this;
+  
+  x(): Accessor<T>;
+  x(fn: Accessor<T>): this;
+  
+  y(): Accessor<T>;
+  y(fn: Accessor<T>): this;
+}
+
+export default function exponential<T = DataPoint>(): ExponentialRegression<T> {
+  let y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+    x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+    domain: Domain;
+  
+  const exponentialRegression = function (data: T[]): ExponentialOutput {
+    let n = 0,
+      Y = 0,
+      YL = 0,
+      XY = 0,
+      XYL = 0,
+      X2Y = 0,
+      xmin = domain ? +domain[0] : Infinity,
+      xmax = domain ? +domain[1] : -Infinity;
+    
+    visitPoints(data, x, y, (dx, dy) => {
+      const ly = Math.log(dy),
+        xy = dx * dy;
+      ++n;
+      Y += (dy - Y) / n;
+      XY += (xy - XY) / n;
+      X2Y += (dx * xy - X2Y) / n;
+      YL += (dy * ly - YL) / n;
+      XYL += (xy * ly - XYL) / n;
+      
+      if (!domain) {
+        if (dx < xmin) xmin = dx;
+        if (dx > xmax) xmax = dx;
+      }
+    });
+    
+    let [a, b] = ols(XY / Y, YL / Y, XYL / Y, X2Y / Y);
+    a = Math.exp(a);
+    
+    const fn = (xx: number) => a * Math.exp(b * xx);
+    const out = <ExponentialOutput>interpose(xmin, xmax, fn);
+    
+    out.a = a;
+    out.b = b;
+    out.predict = fn;
+    out.rSquared = determination(data, x, y, Y, fn);
+    
+    return out;
+  } as ExponentialRegression<T>;
+  
+  exponentialRegression.domain = function (arr) {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return exponentialRegression;
+  } as ExponentialRegression<T>['domain'];
+  
+  exponentialRegression.x = function (fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return exponentialRegression;
+  } as ExponentialRegression<T>['x'];
+  
+  exponentialRegression.y = function (fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return exponentialRegression;
+  } as ExponentialRegression<T>['y'];
+  
+  return exponentialRegression;
+}
diff --git a/src/linear.js b/src/linear.js
deleted file mode 100644
index 64eb61e..0000000
--- a/src/linear.js
+++ /dev/null
@@ -1,57 +0,0 @@
-import { determination } from "./utils/determination";
-import { ols } from "./utils/ols";
-import { visitPoints } from "./utils/points";
-
-export default function(){
-  let x = d => d[0],
-      y = d => d[1],
-      domain;
-
-  function linear(data){
-    let n = 0,
-        X = 0, // sum of x
-        Y = 0, // sum of y
-        XY = 0, // sum of x * y
-        X2 = 0, // sum of x * x
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-
-    visitPoints(data, x, y, (dx, dy) => {
-      ++n;
-      X += (dx - X) / n;
-      Y += (dy - Y) / n;
-      XY += (dx * dy - XY) / n;
-      X2 += (dx * dx - X2) / n;
-      
-      if (!domain){
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    const [intercept, slope] = ols(X, Y, XY, X2),
-        fn = x => slope * x + intercept,
-        out = [[xmin, fn(xmin)], [xmax, fn(xmax)]];
-    
-    out.a = slope;
-    out.b = intercept;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-
-    return out;
-  }
-
-  linear.domain = function(arr){
-    return arguments.length ? (domain = arr, linear) : domain;
-  }
-
-  linear.x = function(fn){
-    return arguments.length ? (x = fn, linear) : x;
-  }
-
-  linear.y = function(fn){
-    return arguments.length ? (y = fn, linear) : y;
-  }
-
-  return linear;
-}
diff --git a/src/linear.ts b/src/linear.ts
new file mode 100644
index 0000000..d9c8e21
--- /dev/null
+++ b/src/linear.ts
@@ -0,0 +1,85 @@
+import { determination } from "./utils/determination";
+import { ols } from "./utils/ols";
+import { visitPoints } from "./utils/points";
+import { PredictFunction, Accessor, DataPoint, Domain } from "./types";
+
+export type LinearOutput = [DataPoint, DataPoint] & {
+  a: number;               // slope
+  b: number;               // intercept
+  predict: PredictFunction
+  rSquared: number;
+};
+
+
+export interface LinearRegression<T>  {
+  (data: T[]): LinearOutput;
+
+  domain(): Domain;
+  domain(arr: Domain): this;
+
+  x(): Accessor<T>;
+  x(fn: Accessor<T>): this;
+
+  y(): Accessor<T>;
+  y(fn: Accessor<T>): this;
+}
+
+export default function linear<T = DataPoint>(): LinearRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+      y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+      domain: Domain;
+
+  const linearRegression = function (data: T[]): LinearOutput {
+    let n = 0,
+      X = 0,  // sum of x
+      Y = 0,  // sum of y
+      XY = 0, // sum of x*y
+      X2 = 0, // sum of x*x
+      xmin = domain ? +domain[0] : Infinity,
+      xmax = domain ? +domain[1] : -Infinity;
+    
+    visitPoints(data, x, y, (dx, dy) => {
+      ++n;
+      X += (dx - X) / n;
+      Y += (dy - Y) / n;
+      XY += (dx * dy - XY) / n;
+      X2 += (dx * dx - X2) / n;
+
+      if (!domain) {
+        if (dx < xmin) xmin = dx;
+        if (dx > xmax) xmax = dx;
+      }
+    });
+
+    const [intercept, slope] = ols(X, Y, XY, X2);
+    const fn = (xx: number) => slope * xx + intercept;
+
+    const out = [[xmin, fn(xmin)], [xmax, fn(xmax)]] as LinearOutput;
+    out.a = slope;
+    out.b = intercept;
+    out.predict = fn;
+    out.rSquared = determination(data, x, y, Y, fn);
+    
+    return out;
+  } as LinearRegression<T>
+  
+  linearRegression.domain = function (arr) {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return linearRegression;
+  } as LinearRegression<T>['domain'];
+
+  linearRegression.x = function (fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return linearRegression;
+  } as LinearRegression<T>['x'];
+
+  linearRegression.y = function (fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return linearRegression;
+  } as LinearRegression<T>['y'];
+
+  return linearRegression;
+}
diff --git a/src/loess.js b/src/loess.js
deleted file mode 100644
index 394b9bc..0000000
--- a/src/loess.js
+++ /dev/null
@@ -1,136 +0,0 @@
-// Adapted from science.js by Jason Davies
-// License: https://github.com/jasondavies/science.js/blob/master/LICENSE
-// Source: https://github.com/jasondavies/science.js/blob/master/src/stats/loess.js
-// Adapted from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/loess.js
-import { median } from "./utils/median";
-import { ols } from "./utils/ols";
-import { points } from "./utils/points";
-
-const maxiters = 2, epsilon = 1e-12;
-
-export default function() {
-  let x = d => d[0],
-      y = d => d[1],
-      bandwidth = .3;
-
-  function loess(data) {
-    const [xv, yv, ux, uy] = points(data, x, y, true),
-          n = xv.length,
-          bw = Math.max(2, ~~(bandwidth * n)), // # nearest neighbors
-          yhat = new Float64Array(n),
-          residuals = new Float64Array(n),
-          robustWeights = new Float64Array(n).fill(1);
-
-    for (let iter = -1; ++iter <= maxiters; ) {
-      const interval = [0, bw - 1];
-
-      for (let i = 0; i < n; ++i) {
-        const dx = xv[i],
-              i0 = interval[0],
-              i1 = interval[1],
-              edge = (dx - xv[i0]) > (xv[i1] - dx) ? i0 : i1;
-
-        let W = 0, X = 0, Y = 0, XY = 0, X2 = 0,
-            denom = 1 / Math.abs(xv[edge] - dx || 1); // Avoid singularity
-
-        for (let k = i0; k <= i1; ++k) {
-          const xk = xv[k],
-                yk = yv[k],
-                w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k],
-                xkw = xk * w;
-
-          W += w;
-          X += xkw;
-          Y += yk * w;
-          XY += yk * xkw;
-          X2 += xk * xkw;
-        }
-
-        // Linear regression fit
-        const [a, b] = ols(X / W, Y / W, XY / W, X2 / W);
-        yhat[i] = a + b * dx;
-        residuals[i] = Math.abs(yv[i] - yhat[i]);
-
-        updateInterval(xv, i + 1, interval);
-      }
-
-      if (iter === maxiters) {
-        break;
-      }
-
-      const medianResidual = median(residuals);
-      if (Math.abs(medianResidual) < epsilon) break;
-
-      for (let i = 0, arg, w; i < n; ++i){
-        arg = residuals[i] / (6 * medianResidual);
-        // Default to epsilon (rather than zero) for large deviations
-        // Keeping weights tiny but non-zero prevents singularites
-        robustWeights[i] = (arg >= 1) ? epsilon : ((w = 1 - arg * arg) * w);
-      }
-    }
-
-    return output(xv, yhat, ux, uy);
-  }
-
-  loess.bandwidth = function(bw) {
-    return arguments.length ? (bandwidth = bw, loess) : bandwidth;
-  };
-
-  loess.x = function(fn) {
-    return arguments.length ? (x = fn, loess) : x;
-  };
-
-  loess.y = function(fn) {
-    return arguments.length ? (y = fn, loess) : y;
-  };
-
-  return loess;
-}
-
-// Weighting kernel for local regression
-function tricube(x) {
-  return (x = 1 - x * x * x) * x * x;
-}
-
-// Advance sliding window interval of nearest neighbors
-function updateInterval(xv, i, interval) {
-  let val = xv[i],
-      left = interval[0],
-      right = interval[1] + 1;
-
-  if (right >= xv.length) return;
-
-  // Step right if distance to new right edge is <= distance to old left edge
-  // Step when distance is equal to ensure movement over duplicate x values
-  while (i > left && (xv[right] - val) <= (val - xv[left])) {
-    interval[0] = ++left;
-    interval[1] = right;
-    ++right;
-  }
-}
-
-// Generate smoothed output points
-// Average points with repeated x values
-function output(xv, yhat, ux, uy) {
-  const n = xv.length, out = [];
-  let i = 0, cnt = 0, prev = [], v;
-
-  for (; i<n; ++i) {
-    v = xv[i] + ux;
-    if (prev[0] === v) {
-      // Average output values via online update
-      prev[1] += (yhat[i] - prev[1]) / (++cnt);
-    } else {
-      // Add new output point
-      cnt = 0;
-      prev[1] += uy;
-      prev = [v, yhat[i]];
-      out.push(prev);
-    }
-  }
-  prev[1] += uy;
-
-  return out;
-}
\ No newline at end of file
diff --git a/src/loess.ts b/src/loess.ts
new file mode 100644
index 0000000..5b9ee5d
--- /dev/null
+++ b/src/loess.ts
@@ -0,0 +1,155 @@
+// Adapted from science.js by Jason Davies
+// License: https://github.com/jasondavies/science.js/blob/master/LICENSE
+// Source: https://github.com/jasondavies/science.js/blob/master/src/stats/loess.js
+// Adapted from vega-statistics by Jeffrey Heer
+// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+// Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/loess.js
+
+import { median } from "./utils/median";
+import { ols } from "./utils/ols";
+import { points } from "./utils/points";
+import { Accessor, DataPoint } from "./types";
+
+const maxiters = 2, epsilon = 1e-12;
+
+
+export interface LoessRegression<T>  {
+  (data: T[]):  DataPoint[];
+
+  bandwidth(): number;
+  bandwidth(bw: number): this;
+
+  x(): Accessor<T>;
+  x(fn: Accessor<T>): this;
+
+  y(): Accessor<T>;
+  y(fn: Accessor<T>): this;
+}
+
+export default function loess<T = DataPoint>(): LoessRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+      y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+      bandwidth = 0.3;
+
+  const loessRegression = function loessRegression(data: T[]): Array<DataPoint> {
+    const [xv, yv, ux, uy] = points(data, (dd) => x(dd), (dd) => y(dd), true);
+    const n = xv.length;
+    const bw = Math.max(2, ~~(bandwidth * n)); // # of nearest neighbors
+    const yhat = new Float64Array(n);
+    const residuals = new Float64Array(n);
+    const robustWeights = new Float64Array(n).fill(1);
+    
+    for (let iter = -1; ++iter <= maxiters;) {
+      const interval = [0, bw - 1] as [number, number];
+      
+      for (let i = 0; i < n; ++i) {
+        const dx = xv[i];
+        const i0 = interval[0];
+        const i1 = interval[1];
+        const edge = (dx - xv[i0]) > (xv[i1] - dx) ? i0 : i1;
+        
+        let W = 0, X = 0, Y = 0, XY = 0, X2 = 0;
+        const denom = 1 / Math.abs(xv[edge] - dx || 1);
+        
+        for (let k = i0; k <= i1; ++k) {
+          const xk = xv[k];
+          const yk = yv[k];
+          const w = tricube(Math.abs(dx - xk) * denom) * robustWeights[k];
+          const xkw = xk * w;
+          W += w;
+          X += xkw;
+          Y += yk * w;
+          XY += yk * xkw;
+          X2 += xk * xkw;
+        }
+        
+        // Linear regression fit
+        const [a, b] = ols(X / W, Y / W, XY / W, X2 / W);
+        yhat[i] = a + b * dx;
+        residuals[i] = Math.abs(yv[i] - yhat[i]);
+        
+        updateInterval(xv, i + 1, interval);
+      }
+      
+      if (iter === maxiters) {
+        break;
+      }
+      
+      const medianResidual = median(residuals);
+      if (Math.abs(medianResidual) < epsilon) break;
+      
+      for (let i = 0, arg, w; i < n; ++i) {
+        arg = residuals[i] / (6 * medianResidual);
+        // Default to epsilon (rather than zero) for large deviations
+        // Keeping weights tiny but non-zero prevents singularites
+        robustWeights[i] = (arg >= 1) ? epsilon : ((w = 1 - arg * arg) * w);
+      }
+    }
+    
+    return output(xv, yhat, ux, uy);
+  } as LoessRegression<T>
+  
+  loessRegression.bandwidth = function (bw?: number) {
+    if (!arguments.length) return bandwidth;
+    bandwidth = bw!;
+    return loessRegression;
+  } as LoessRegression<T>['bandwidth'];
+
+  loessRegression.x = function (fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return loessRegression;
+  } as LoessRegression<T>['x'];
+
+  loessRegression.y = function (fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return loessRegression;
+  } as LoessRegression<T>['y'];
+
+  return loessRegression;
+}
+
+// Weighting kernel for local regression
+function tricube(x: number): number {
+  return (x = 1 - x * x * x) * x * x;
+}
+
+// Advance sliding window interval of nearest neighbors
+function updateInterval(xv: Float64Array, i: number, interval: [number, number]) {
+  let val = xv[i], left = interval[0], right = interval[1] + 1;
+  if (right >= xv.length) return;
+  
+  // Step right if distance to new right edge is <= distance to old left edge
+  // Step when distance is equal to ensure movement over duplicate x values
+  while (i > left && (xv[right] - val) <= (val - xv[left])) {
+    interval[0] = ++left;
+    interval[1] = right;
+    ++right;
+  }
+}
+
+// Generate smoothed output points
+// Average points with repeated x values
+function output(xv: Float64Array, yhat: Float64Array, ux: number, uy: number): Array<[number, number]> {
+  const n = xv.length, out = [];
+  
+  let i = 0, cnt = 0, prev: any = [], v;
+  
+  for (; i < n; ++i) {
+    v = xv[i] + ux;
+    if (prev[0] === v) {
+      // Average output values via online update
+      prev[1] += (yhat[i] - prev[1]) / (++cnt);
+    } else {
+      // Add new output point
+      cnt = 0;
+      prev[1] += uy;
+      prev = [v, yhat[i]];
+      out.push(prev);
+    }
+  }
+  prev[1] += uy;
+  
+  return out;
+}
diff --git a/src/logarithmic.js b/src/logarithmic.js
deleted file mode 100644
index 1d14d37..0000000
--- a/src/logarithmic.js
+++ /dev/null
@@ -1,65 +0,0 @@
-import { determination } from "./utils/determination";
-import { interpose } from "./utils/interpose";
-import { ols } from "./utils/ols";
-import { visitPoints } from "./utils/points";
-
-export default function() {
-  let x = d => d[0],
-      y = d => d[1],
-      base = Math.E,
-      domain;
-  
-  function logarithmic(data){
-    let n = 0,
-        X = 0,
-        Y = 0,
-        XY = 0,
-        X2 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity,
-        lb = Math.log(base);
-    
-    visitPoints(data, x, y, (dx, dy) => {
-      const lx = Math.log(dx) / lb;
-      ++n;
-      X += (lx - X) / n;
-      Y += (dy - Y) / n;
-      XY += (lx * dy - XY) / n;
-      X2 += (lx * lx - X2) / n;
-      
-      if (!domain){
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-    
-    const [intercept, slope] = ols(X, Y, XY, X2),
-        fn = x => slope * Math.log(x) / lb + intercept,
-        out = interpose(xmin, xmax, fn);
-        
-    out.a = slope;
-    out.b = intercept;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-
-    return out; 
-  }
-  
-  logarithmic.domain = function(arr){
-    return arguments.length ? (domain = arr, logarithmic) : domain;
-  }
-
-  logarithmic.x = function(fn){
-    return arguments.length ? (x = fn, logarithmic) : x;
-  }
-
-  logarithmic.y = function(fn){
-    return arguments.length ? (y = fn, logarithmic) : y;
-  }
-
-  logarithmic.base = function(n){
-    return arguments.length ? (base = n, logarithmic) : base;
-  }
-  
-  return logarithmic;
-}
\ No newline at end of file
diff --git a/src/logarithmic.ts b/src/logarithmic.ts
new file mode 100644
index 0000000..f135217
--- /dev/null
+++ b/src/logarithmic.ts
@@ -0,0 +1,99 @@
+import { determination } from "./utils/determination";
+import { interpose } from "./utils/interpose";
+import { ols } from "./utils/ols";
+import { visitPoints } from "./utils/points";
+import { PredictFunction, DataPoint, Accessor, Domain } from "./types";
+
+
+type LogarithmicOutput = [DataPoint, DataPoint] & {
+  a: number;               // slope
+  b: number;               // intercept
+  predict: PredictFunction
+  rSquared: number;
+};
+
+
+export interface LogarithmicRegression<T>  {
+  (data: T[]): LogarithmicOutput;
+  
+  domain(): Domain;
+  domain(arr: Domain): this;
+  
+  x(): Accessor<T>;
+  x(fn: Accessor<T>): this;
+  
+  y(): Accessor<T>;
+  y(fn: Accessor<T>): this;
+  
+  base(): number;
+  base(b: number): this;
+}
+
+export default function logarithmic<T = DataPoint>(): LogarithmicRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+    y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+    base: number = Math.E,
+    domain: Domain;
+  
+  const logarithmicRegression = function (data: T[]): LogarithmicOutput {
+    let n = 0,
+      X = 0,
+      Y = 0,
+      XY = 0,
+      X2 = 0,
+      xmin = domain ? +domain[0] : Infinity,
+      xmax = domain ? +domain[1] : -Infinity,
+      lb = Math.log(base);
+    
+    visitPoints(data, x, y, (dx, dy) => {
+      const lx = Math.log(dx) / lb;
+      ++n;
+      X += (lx - X) / n;
+      Y += (dy - Y) / n;
+      XY += (lx * dy - XY) / n;
+      X2 += (lx * lx - X2) / n;
+      
+      if (!domain) {
+        if (dx < xmin) xmin = dx;
+        if (dx > xmax) xmax = dx;
+      }
+    });
+    
+    const [intercept, slope] = ols(X, Y, XY, X2);
+    const fn = (xx: number) => slope * Math.log(xx) / lb + intercept;
+    const out = interpose(xmin, xmax, fn) as LogarithmicOutput;
+    
+    out.a = slope;
+    out.b = intercept;
+    out.predict = fn;
+    out.rSquared = determination(data, x, y, Y, fn);
+    
+    return out;
+  } as LogarithmicRegression<T>;
+  
+  logarithmicRegression.domain = function (arr?: [number, number]) {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return logarithmicRegression;
+  } as LogarithmicRegression<T>['domain'];
+  
+  logarithmicRegression.x = function (fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return logarithmicRegression;
+  } as LogarithmicRegression<T>['x'];
+  
+  logarithmicRegression.y = function (fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return logarithmicRegression;
+  } as LogarithmicRegression<T>['y'];
+  
+  logarithmicRegression.base = function (b?: number) {
+    if (!arguments.length) return base;
+    base = b!;
+    return logarithmicRegression;
+  } as LogarithmicRegression<T>['base'];
+  
+  return logarithmicRegression;
+}
diff --git a/src/polynomial.js b/src/polynomial.js
deleted file mode 100644
index 4b3a650..0000000
--- a/src/polynomial.js
+++ /dev/null
@@ -1,169 +0,0 @@
-import { determination } from "./utils/determination";
-import { interpose } from "./utils/interpose";
-import { points, visitPoints } from "./utils/points";
-import linear from "./linear";
-import quad from "./quadratic";
-
-// Adapted from regression-js by Tom Alexander
-// Source: https://github.com/Tom-Alexander/regression-js/blob/master/src/regression.js#L246
-// License: https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE
-// ...with ideas from vega-statistics by Jeffrey Heer
-// Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/poly.js
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-export default function(){
-  let x = d => d[0],
-      y = d => d[1],
-      order = 3,
-      domain;
-  
-  function polynomial(data) {    
-    // Use more efficient methods for lower orders
-    if (order === 1) {
-      const o = linear().x(x).y(y).domain(domain)(data);
-      o.coefficients = [o.b, o.a];
-      delete o.a; delete o.b;
-      return o;
-    }
-    if (order === 2) {
-      const o = quad().x(x).y(y).domain(domain)(data);
-      o.coefficients = [o.c, o.b, o.a];
-      delete o.a; delete o.b; delete o.c;
-      return o;
-    }
-    
-    const [xv, yv, ux, uy] = points(data, x, y),
-        n = xv.length,
-        lhs = [],
-        rhs = [],
-        k = order + 1;
-
-    let Y = 0, n0 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    
-    visitPoints(data, x, y, (dx, dy) => {
-      ++n0
-      Y += (dy - Y) / n0;
-      if (!domain){
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;        
-      }
-    });
-
-    let i, j, l, v, c;
-
-    for (i = 0; i < k; ++i) {
-      for (l = 0, v = 0; l < n; ++l) {
-        v += Math.pow(xv[l], i) * yv[l];
-      }
-      lhs.push(v);
-
-      c = new Float64Array(k);
-      for (j=0; j<k; ++j) {
-        for (l=0, v=0; l<n; ++l) {
-          v += Math.pow(xv[l], i + j);
-        }
-        c[j] = v;
-      }
-      rhs.push(c);
-    }
-    rhs.push(lhs);
-
-    const coef = gaussianElimination(rhs),
-          fn = x => {
-            x -= ux;
-            let y = uy + coef[0] + coef[1] * x + coef[2] * x * x;
-            for (i = 3; i < k; ++i) y += coef[i] * Math.pow(x, i);
-            return y;
-          },
-          out = interpose(xmin, xmax, fn);
-
-    out.coefficients = uncenter(k, coef, -ux, uy);
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-    
-    return out;
-  }
-
-  polynomial.domain = function(arr){
-    return arguments.length ? (domain = arr, polynomial) : domain;
-  }
-
-  polynomial.x = function(fn){
-    return arguments.length ? (x = fn, polynomial) : x;
-  }
-
-  polynomial.y = function(fn){
-    return arguments.length ? (y = fn, polynomial) : y;
-  }
-
-  polynomial.order = function(n){
-    return arguments.length ? (order = n, polynomial) : order;
-  }
-  
-  return polynomial;
-}
-
-function uncenter(k, a, x, y) {
-  const z = Array(k);
-  let i, j, v, c;
-
-  // initialize to zero
-  for (i = 0; i < k; ++i) z[i] = 0;
-
-  // polynomial expansion
-  for (i = k - 1; i >= 0; --i) {
-    v = a[i];
-    c = 1;
-    z[i] += v;
-    for (j = 1; j <= i; ++j) {
-      c *= (i + 1 - j) / j; // binomial coefficent
-      z[i-j] += v * Math.pow(x, j) * c;
-    }
-  }
-
-  // bias term
-  z[0] += y;
-
-  return z;
-}
-
-// Given an array for a two-dimensional matrix and the polynomial order,
-// solve A * x = b using Gaussian elimination.
-function gaussianElimination(matrix) {
-  const n = matrix.length - 1,
-        coef = [];
-
-  let i, j, k, r, t;
-
-  for (i = 0; i < n; ++i) {
-    r = i; // max row
-    for (j = i + 1; j < n; ++j) {
-      if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
-        r = j;
-      }
-    }
-
-    for (k = i; k < n + 1; ++k) {
-      t = matrix[k][i];
-      matrix[k][i] = matrix[k][r];
-      matrix[k][r] = t;
-    }
-
-    for (j = i + 1; j < n; ++j) {
-      for (k = n; k >= i; k--) {
-        matrix[k][j] -= (matrix[k][i] * matrix[i][j]) / matrix[i][i];
-      }
-    }
-  }
-
-  for (j = n - 1; j >= 0; --j) {
-    t = 0;
-    for (k = j + 1; k < n; ++k) {
-      t += matrix[k][j] * coef[k];
-    }
-    coef[j] = (matrix[n][j] - t) / matrix[j][j];
-  }
-
-  return coef;
-}
\ No newline at end of file
diff --git a/src/polynomial.ts b/src/polynomial.ts
new file mode 100644
index 0000000..fb3fc6c
--- /dev/null
+++ b/src/polynomial.ts
@@ -0,0 +1,203 @@
+// Adapted from regression-js by Tom Alexander
+// Source: https://github.com/Tom-Alexander/regression-js/blob/master/src/regression.js#L246
+// License: https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE
+// ...with ideas from vega-statistics by Jeffrey Heer
+// Source: https://github.com/vega/vega/blob/f21cb8792b4e0cbe2b1a3fd44b0f5db370dbaadb/packages/vega-statistics/src/regression/poly.js
+// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+
+
+import { determination } from "./utils/determination";
+import { interpose } from "./utils/interpose";
+import { points, visitPoints } from "./utils/points";
+import linear from "./linear";
+import quad from "./quadratic";
+import { PredictFunction, Accessor, DataPoint, Domain } from "./types";
+
+export type PolynomialOutput = [DataPoint, DataPoint] & {
+  coefficients: number[];
+  predict: PredictFunction;
+  rSquared: number;
+};
+
+
+export interface PolynomialRegression<T>  {
+  (data: T[]): PolynomialOutput;
+
+  domain(): Domain;
+  domain(domain?: Domain): this;
+
+  x(): Accessor<T>;
+  x(x: Accessor<T>): this;
+
+  y(): Accessor<T>;
+  y(y: Accessor<T>): this;
+
+  order(): number;
+  order(order: number): this;
+}
+
+export default function polynomial<T = DataPoint>(): PolynomialRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+      y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+      order = 3,
+      domain: Domain;
+
+  const polynomialRegression = function polynomialRegression(data: T[]): PolynomialOutput {
+    // Shortcut for lower-order polynomials:
+    if (order === 1) {
+      const o = linear<T>().x(x).y(y).domain(domain)(data);
+      const result = [o[0], o[1]] as PolynomialOutput;
+      result.coefficients = [o.b, o.a];
+      result.predict = o.predict;
+      result.rSquared = o.rSquared;
+      return result;
+    }
+    if (order === 2) {
+      const o = quad<T>().x(x).y(y).domain(domain)(data);
+      const result = [o[0], o[1]] as PolynomialOutput;
+      result.coefficients = [o.c, o.b, o.a];
+      result.predict = o.predict;
+      result.rSquared = o.rSquared;
+      return result;
+    }
+    
+    const [xv, yv, ux, uy] = points(data, x, y);
+    const n = xv.length;
+    const k = order + 1;
+    const lhs: number[] = [];
+    const rhs: Float64Array[] = [];
+
+    let Y = 0,
+      n0 = 0,
+      xmin = domain ? +domain[0] : Infinity,
+      xmax = domain ? +domain[1] : -Infinity;
+    
+    visitPoints(data, x, y, (dx, dy) => {
+      n0++;
+      Y += (dy - Y) / n0;
+      if (!domain) {
+        if (dx < xmin) xmin = dx;
+        if (dx > xmax) xmax = dx;
+      }
+    });
+
+    // Build normal equations
+    for (let i = 0; i < k; i++) {
+      // LHS
+      let v = 0;
+      for (let l = 0; l < n; l++) {
+        v += Math.pow(xv[l], i) * yv[l];
+      }
+      lhs.push(v);
+
+      // RHS
+      const c = new Float64Array(k);
+      for (let j = 0; j < k; j++) {
+        let v2 = 0;
+        for (let l = 0; l < n; l++) {
+          v2 += Math.pow(xv[l], i + j);
+        }
+        c[j] = v2;
+      }
+      rhs.push(c);
+    }
+    rhs.push(new Float64Array(lhs));
+
+    const coef = gaussianElimination(rhs);
+    const fn = (xx: number) => {
+      let shifted = xx - ux;
+      let val = uy + coef[0];
+      for (let i = 1; i < k; i++) {
+        val += coef[i] * Math.pow(shifted, i);
+      }
+      return val;
+    };
+    
+    const out = <PolynomialOutput>interpose(xmin, xmax, fn);
+    out.coefficients = uncenter(k, coef, -ux, uy);
+    out.predict = fn;
+    out.rSquared = determination(data, x, y, Y, fn);
+    
+    return out;
+  } as PolynomialRegression<T>;
+  
+  polynomialRegression.domain = function (arr?: [number, number]) {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return polynomialRegression;
+  } as PolynomialRegression<T>['domain'];
+
+  polynomialRegression.x = function (fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return polynomialRegression;
+  } as PolynomialRegression<T>['x'];
+
+  polynomialRegression.y = function (fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return polynomialRegression;
+  } as PolynomialRegression<T>['y'];
+
+  polynomialRegression.order = function (n?: number) {
+    if (!arguments.length) return order;
+    order = n!;
+    return polynomialRegression;
+  } as PolynomialRegression<T>['order'];
+
+  return polynomialRegression;
+}
+
+function uncenter(k: number, a: number[], x: number, y: number): number[] {
+  const z = new Array<number>(k).fill(0);
+  for (let i = k - 1; i >= 0; --i) {
+    let v = a[i];
+    z[i] += v;
+    let c = 1;
+    for (let j = 1; j <= i; ++j) {
+      c *= (i + 1 - j) / j; // binomial coefficient
+      z[i - j] += v * Math.pow(x, j) * c;
+    }
+  }
+  // bias term
+  z[0] += y;
+  return z;
+}
+
+// Solve A * x = b using Gaussian elimination
+function gaussianElimination(matrix: Float64Array[]): number[] {
+  const n = matrix.length - 1;
+  const coef = new Array<number>(n);
+
+  for (let i = 0; i < n; i++) {
+    let r = i;
+    // find pivot row
+    for (let j = i + 1; j < n; j++) {
+      if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) {
+        r = j;
+      }
+    }
+    // swap columns
+    for (let k = i; k < n + 1; k++) {
+      const t = matrix[k][i];
+      matrix[k][i] = matrix[k][r];
+      matrix[k][r] = t;
+    }
+    // reduce
+    for (let j = i + 1; j < n; j++) {
+      for (let k = n; k >= i; k--) {
+        matrix[k][j] -= (matrix[k][i] * matrix[i][j]) / matrix[i][i];
+      }
+    }
+  }
+  
+  for (let j = n - 1; j >= 0; j--) {
+    let t = 0;
+    for (let k = j + 1; k < n; k++) {
+      t += matrix[k][j] * coef[k];
+    }
+    coef[j] = (matrix[n][j] - t) / matrix[j][j];
+  }
+  
+  return coef;
+}
diff --git a/src/power.js b/src/power.js
deleted file mode 100644
index 589efed..0000000
--- a/src/power.js
+++ /dev/null
@@ -1,63 +0,0 @@
-import { determination } from "./utils/determination";
-import { interpose } from "./utils/interpose";
-import { ols } from "./utils/ols";
-import { visitPoints } from "./utils/points";
-
-export default function() {
-  let x = d => d[0],
-      y = d => d[1],
-      domain;
-  
-  function power(data){
-    let n = 0,
-        X = 0,
-        Y = 0,
-        XY = 0,
-        X2 = 0,
-        YS = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    
-    visitPoints(data, x, y, (dx, dy) => {
-      const lx = Math.log(dx),
-            ly = Math.log(dy);
-      ++n;
-      X += (lx - X) / n;
-      Y += (ly - Y) / n;
-      XY += (lx * ly - XY) / n;
-      X2 += (lx * lx - X2) / n;
-      YS += (dy - YS) / n;
-
-      if (!domain){
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-
-    let [a, b] = ols(X, Y, XY, X2);
-    a = Math.exp(a);
-    const fn = x => a * Math.pow(x, b),
-        out = interpose(xmin, xmax, fn);
-
-    out.a = a;
-    out.b = b;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, YS, fn);
-
-    return out; 
-  }
-  
-  power.domain = function(arr){
-    return arguments.length ? (domain = arr, power) : domain;
-  }
-
-  power.x = function(fn){
-    return arguments.length ? (x = fn, power) : x;
-  }
-
-  power.y = function(fn){
-    return arguments.length ? (y = fn, power) : y;
-  }
-  
-  return power;
-}
\ No newline at end of file
diff --git a/src/power.ts b/src/power.ts
new file mode 100644
index 0000000..54a140e
--- /dev/null
+++ b/src/power.ts
@@ -0,0 +1,92 @@
+import { determination } from "./utils/determination";
+import { interpose } from "./utils/interpose";
+import { ols } from "./utils/ols";
+import { visitPoints } from "./utils/points";
+import { PredictFunction, DataPoint, Accessor, Domain } from "./types";
+
+export type PowerOutput = [DataPoint, DataPoint] & {
+  a: number;
+  b: number;
+  predict: PredictFunction;
+  rSquared: number;
+}
+
+
+interface PowerRegression<T>  {
+  (data: T[]): PowerOutput;
+
+  domain(): Domain;
+  domain(domain?: Domain): this;
+
+  x(): Accessor<T>;
+  x(x: Accessor<T>): this;
+
+  y(): Accessor<T>;
+  y(y: Accessor<T>): this;
+}
+
+export default function power<T = DataPoint>(): PowerRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+      y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+      domain: Domain;
+
+  const powerRegression = function powerRegression(data: T[]): PowerOutput {
+    let n = 0,
+        X = 0,
+        Y = 0,
+        XY = 0,
+        X2 = 0,
+        YS = 0,
+        xmin = domain ? +domain[0] : Infinity,
+        xmax = domain ? +domain[1] : -Infinity;
+
+    visitPoints(data, x, y, (dx, dy) => {
+      const lx = Math.log(dx),
+            ly = Math.log(dy);
+      ++n;
+      X += (lx - X) / n;
+      Y += (ly - Y) / n;
+      XY += (lx * ly - XY) / n;
+      X2 += (lx * lx - X2) / n;
+      YS += (dy - YS) / n;
+
+      if (!domain) {
+        if (dx < xmin) xmin = dx;
+        if (dx > xmax) xmax = dx;
+      }
+    });
+
+    let [a, b] = ols(X, Y, XY, X2);
+    a = Math.exp(a);
+
+    const fn = (xx: number) => a * Math.pow(xx, b);
+    const out = interpose(xmin, xmax, fn) as PowerOutput;
+
+    out.a = a;
+    out.b = b;
+    out.predict = fn;
+    out.rSquared = determination(data, x, y, YS, fn);
+
+    return out;
+  } as PowerRegression<T>;
+
+  powerRegression.domain = function(arr?: Domain) {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return powerRegression;
+  } as PowerRegression<T>['domain'];
+
+  powerRegression.x = function(fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return powerRegression;
+  } as PowerRegression<T>['x'];
+
+  powerRegression.y = function(fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return powerRegression;
+  } as PowerRegression<T>['y'];
+
+  return powerRegression;
+}
diff --git a/src/quadratic.js b/src/quadratic.js
deleted file mode 100644
index 34109f1..0000000
--- a/src/quadratic.js
+++ /dev/null
@@ -1,80 +0,0 @@
-import { determination } from "./utils/determination";
-import { interpose } from "./utils/interpose";
-import { points, visitPoints } from "./utils/points";
-
-export default function(){
-  let x = d => d[0],
-      y = d => d[1],
-      domain;
-
-  function quadratic(data){
-    const [xv, yv, ux, uy] = points(data, x, y),
-          n = xv.length;
-    
-    let X2 = 0,
-        X3 = 0,
-        X4 = 0,
-        XY = 0,
-        X2Y = 0,
-        i, dx, dy, x2;
-
-    for (i = 0; i < n;) {
-      dx = xv[i];
-      dy = yv[i++];
-      x2 = dx * dx;
-      X2 += (x2 - X2) / i;
-      X3 += (x2 * dx - X3) / i;
-      X4 += (x2 * x2 - X4) / i;
-      XY += (dx * dy - XY) / i;
-      X2Y += (x2 * dy - X2Y) / i;
-    }
-    
-    let Y = 0,
-        n0 = 0,
-        xmin = domain ? +domain[0] : Infinity,
-        xmax = domain ? +domain[1] : -Infinity;
-    
-    visitPoints(data, x, y, (dx, dy) => {
-      n0++;
-      Y += (dy - Y) / n0;
-      if (!domain){
-        if (dx < xmin) xmin = dx;
-        if (dx > xmax) xmax = dx;
-      }
-    });
-    
-    const X2X2 = X4 - (X2 * X2),
-          d = (X2 * X2X2 - X3 * X3),
-          a = (X2Y * X2 - XY * X3) / d,
-          b = (XY * X2X2 - X2Y * X3) / d,
-          c = -a * X2,
-          fn = x => {
-            x = x - ux;
-            return a * x * x + b * x + c + uy;
-          };
-    
-    const out = interpose(xmin, xmax, fn);
-
-    out.a = a;
-    out.b = b - 2 * a * ux;
-    out.c = c - b * ux + a * ux * ux + uy;
-    out.predict = fn;
-    out.rSquared = determination(data, x, y, Y, fn);
-
-    return out;
-  }
-  
-  quadratic.domain = function(arr){
-    return arguments.length ? (domain = arr, quadratic) : domain;
-  }
-
-  quadratic.x = function(fn){
-    return arguments.length ? (x = fn, quadratic) : x;
-  }
-
-  quadratic.y = function(fn){
-    return arguments.length ? (y = fn, quadratic) : y;
-  }
-  
-  return quadratic;
-}
\ No newline at end of file
diff --git a/src/quadratic.ts b/src/quadratic.ts
new file mode 100644
index 0000000..3f91ffa
--- /dev/null
+++ b/src/quadratic.ts
@@ -0,0 +1,108 @@
+import { determination } from "./utils/determination";
+import { interpose } from "./utils/interpose";
+import { points, visitPoints } from "./utils/points";
+import { Accessor, DataPoint, PredictFunction, Domain } from "./types";
+
+export type QuadraticOutput = [DataPoint, DataPoint] & {
+  a: number;
+  b: number;
+  c: number;
+  predict: PredictFunction;
+  rSquared: number;
+}
+
+
+interface QuadraticRegression<T>  {
+  (data: T[]): QuadraticOutput;
+
+  domain(): Domain;
+  domain(domain?: Domain): this;
+
+  x(): Accessor<T>;
+  x(x: Accessor<T>): this;
+
+  y(): Accessor<T>;
+  y(y: Accessor<T>): this;
+}
+
+export default function quadratic<T = DataPoint>(): QuadraticRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+      y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+      domain: Domain;
+
+  const quadraticRegression = function quadraticRegression(data: T[]): QuadraticOutput {
+    const [xv, yv, ux, uy] = points(data, (dd) => x(dd), (dd) => y(dd));
+    const n = xv.length;
+
+    let X2 = 0,
+        X3 = 0,
+        X4 = 0,
+        XY = 0,
+        X2Y = 0,
+        i, dx, dy, x2;
+
+    for (i = 0; i < n;) {
+      dx = xv[i];
+      dy = yv[i++];
+      x2 = dx * dx;
+      X2 += (x2 - X2) / i;
+      X3 += ((x2 * dx) - X3) / i;
+      X4 += ((x2 * x2) - X4) / i;
+      XY += ((dx * dy) - XY) / i;
+      X2Y += ((x2 * dy) - X2Y) / i;
+    }
+    
+    let Y = 0,
+      n0 = 0,
+      xmin = domain ? +domain[0] : Infinity,
+      xmax = domain ? +domain[1] : -Infinity;
+    
+    visitPoints(data, x, y, (dx2, dy2) => {
+      n0++;
+      Y += (dy2 - Y) / n0;
+      if (!domain) {
+        if (dx2 < xmin) xmin = dx2;
+        if (dx2 > xmax) xmax = dx2;
+      }
+    });
+
+    const X2X2 = X4 - (X2 * X2);
+    const d = (X2 * X2X2 - X3 * X3);
+    const a = (X2Y * X2 - XY * X3) / d;
+    const b = (XY * X2X2 - X2Y * X3) / d;
+    const c = -a * X2;
+    const fn = (xx: number) => {
+      const shifted = xx - ux;
+      return a * shifted * shifted + b * shifted + c + uy;
+    };
+
+    const out = interpose(xmin, xmax, fn) as QuadraticOutput;
+    out.a = a;
+    out.b = b - 2 * a * ux;
+    out.c = c - b * ux + a * ux * ux + uy;
+    out.predict = fn;
+    out.rSquared = determination(data, x, y, Y, fn);
+    
+    return out;
+  } as QuadraticRegression<T>;
+  
+  quadraticRegression.domain = function (arr?: Domain) {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return quadraticRegression;
+  } as QuadraticRegression<T>['domain'];
+
+  quadraticRegression.x = function (fn?: Accessor<T>) {
+    if (!arguments.length) return x;
+    x = fn!;
+    return quadraticRegression;
+  } as QuadraticRegression<T>['x'];
+
+  quadraticRegression.y = function (fn?: Accessor<T>) {
+    if (!arguments.length) return y;
+    y = fn!;
+    return quadraticRegression;
+  } as QuadraticRegression<T>['y'];
+
+  return quadraticRegression;
+}
diff --git a/src/sigmoidal.ts b/src/sigmoidal.ts
new file mode 100644
index 0000000..a38ec50
--- /dev/null
+++ b/src/sigmoidal.ts
@@ -0,0 +1,133 @@
+import { determination } from "./utils/determination";
+import { interpose } from "./utils/interpose";
+import { visitPoints } from "./utils/points";
+import { PredictFunction, Domain, DataPoint, Accessor } from "./types";
+
+/**
+ * Sigmoidal (logistic) regression of the form:
+ *   f(x) = C + A / (1 + exp(-B * (x - M)))
+ * where A, B, C, M are parameters found via gradient descent.
+ */
+export type SigmoidalOutput = [DataPoint, DataPoint] & {
+  A: number;
+  B: number;
+  C: number;
+  M: number;
+  predict: PredictFunction;
+  rSquared: number;
+};
+
+export interface SigmoidalRegression<T> {
+  (data: T[]): SigmoidalOutput;
+  domain(): Domain;
+  domain(domain: Domain): this;
+  x(): Accessor<T>;
+  x(fn: Accessor<T>): this;
+  y(): Accessor<T>;
+  y(fn: Accessor<T>): this;
+}
+
+export default function sigmoidal<T = DataPoint>(): SigmoidalRegression<T> {
+  let x: Accessor<T> = (d: T) => (d as DataPoint)[0],
+      y: Accessor<T> = (d: T) => (d as DataPoint)[1],
+      domain: Domain,
+      maxIter = 2000,
+      alpha = 1e-4; // learning rate
+
+  function sigmoidalRegression(data: T[]): SigmoidalOutput {
+    let n = 0,
+        Xmin = domain ? +domain[0] : Infinity,
+        Xmax = domain ? +domain[1] : -Infinity,
+        Ymin = Infinity,
+        Ymax = -Infinity,
+        sumY = 0;
+
+    // Gather data & track min/max for domain if not preset
+    visitPoints(data, x, y, (dx, dy) => {
+      n++;
+      sumY += dy;
+      if (!domain) {
+        if (dx < Xmin) Xmin = dx;
+        if (dx > Xmax) Xmax = dx;
+      }
+      if (dy < Ymin) Ymin = dy;
+      if (dy > Ymax) Ymax = dy;
+    });
+
+    if (!domain && (Xmin === Infinity || Xmax === -Infinity)) {
+      Xmin = 0;
+      Xmax = 1;
+    }
+
+    // Initialize parameters
+    let A = Ymax - Ymin;   // amplitude
+    let B = 1;             // logistic slope
+    let C = Ymin;          // baseline offset
+    let M = (Xmin + Xmax) / 2; // midpoint
+
+    // Predict function
+    function f(xx: number) {
+      return C + A / (1 + Math.exp(-B * (xx - M)));
+    }
+
+    // Gradient Descent
+    for (let iter = 0; iter < maxIter; iter++) {
+      let dA = 0, dB = 0, dC = 0, dM = 0;
+      visitPoints(data, x, y, (dx, dy) => {
+        const yhat = f(dx);
+        const err = dy - yhat;
+        const ex = Math.exp(-B * (dx - M));
+        const g = 1 / (1 + ex); // logistic
+        // partial derivatives
+        const df_dA = g;
+        const df_dC = 1;
+        const df_dB = A * g * (1 - g) * (dx - M);
+        // Note the negative sign for M:
+        const df_dM = -A * B * g * (1 - g);
+
+        const factor = -2 * err;
+        dA += factor * df_dA;
+        dB += factor * df_dB;
+        dC += factor * df_dC;
+        dM += factor * df_dM;
+      });
+      A -= alpha * (dA / n);
+      B -= alpha * (dB / n);
+      C -= alpha * (dC / n);
+      M -= alpha * (dM / n);
+    }
+
+    const predict = (xx: number) => C + A / (1 + Math.exp(-B * (xx - M)));
+    const out = interpose(Xmin, Xmax, predict) as SigmoidalOutput;
+    out.A = A;
+    out.B = B;
+    out.C = C;
+    out.M = M;
+    out.predict = predict;
+
+    // R^2
+    const meanY = sumY / n;
+    out.rSquared = determination(data, x, y, meanY, predict);
+    return out;
+  }
+
+  sigmoidalRegression.domain = function(arr?: Domain): any {
+    if (!arguments.length) return domain;
+    domain = arr;
+    return sigmoidalRegression;
+  };
+
+  sigmoidalRegression.x = function(fn?: Accessor<T>): any {
+    if (!arguments.length) return x;
+    x = fn!;
+    return sigmoidalRegression;
+  };
+
+  sigmoidalRegression.y = function(fn?: Accessor<T>): any {
+    if (!arguments.length) return y;
+    y = fn!;
+    return sigmoidalRegression;
+  };
+
+  return sigmoidalRegression;
+}
\ No newline at end of file
diff --git a/src/types.ts b/src/types.ts
new file mode 100644
index 0000000..f31848d
--- /dev/null
+++ b/src/types.ts
@@ -0,0 +1,4 @@
+export type DataPoint = [number, number];
+export type Accessor<T> = (d: T, i?: number, data?: T[]) => number;
+export type PredictFunction = (x: number) => number;
+export type Domain = [number, number] | undefined;
diff --git a/src/utils/determination.js b/src/utils/determination.js
deleted file mode 100644
index 117eaea..0000000
--- a/src/utils/determination.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { visitPoints } from "./points";
-
-// Given a dataset, x- and y-accessors, the mean center of the y values, and a predict function,
-// return the coefficient of determination, or R squared.
-export function determination(data, x, y, uY, predict){
-  let SSE = 0,
-      SST = 0;
-  
-  visitPoints(data, x, y, (dx, dy) => {
-    const sse = dy - predict(dx),
-          sst = dy - uY;
-
-    SSE += sse * sse;
-    SST += sst * sst;
-  });
-
-  return 1 - SSE / SST;
-}
\ No newline at end of file
diff --git a/src/utils/determination.ts b/src/utils/determination.ts
new file mode 100644
index 0000000..1c23e79
--- /dev/null
+++ b/src/utils/determination.ts
@@ -0,0 +1,26 @@
+import { visitPoints } from "./points";
+import { Accessor, DataPoint, PredictFunction } from "../types";
+
+/**
+ * Given a dataset, x- and y-accessors, the mean center of the y-values (uY),
+ * and a prediction function, return the coefficient of determination, R^2.
+ */
+export function determination<T>(
+  data: T[],
+  x: Accessor<T>,
+  y: Accessor<T>,
+  uY: number,
+  predict: PredictFunction
+): number {
+  let SSE = 0, // Sum of Squared Errors
+      SST = 0; // Total Sum of Squares
+
+  visitPoints(data, x, y, (dx, dy) => {
+    const sse = dy - predict(dx);
+    const sst = dy - uY;
+    SSE += sse * sse;
+    SST += sst * sst;
+  });
+
+  return 1 - SSE / SST;
+}
diff --git a/src/utils/geometry.js b/src/utils/geometry.js
deleted file mode 100644
index 29b9134..0000000
--- a/src/utils/geometry.js
+++ /dev/null
@@ -1,9 +0,0 @@
-// Returns the angle of a line in degrees.
-export function angle(line){
-  return Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) * 180 / Math.PI;
-}
-
-// Returns the midpoint of a line.
-export function midpoint(line){
-  return [(line[0][0] + line[1][0]) / 2, (line[0][1] + line[1][1]) / 2];
-}
\ No newline at end of file
diff --git a/src/utils/geometry.ts b/src/utils/geometry.ts
new file mode 100644
index 0000000..e5b75dd
--- /dev/null
+++ b/src/utils/geometry.ts
@@ -0,0 +1,21 @@
+import { DataPoint } from "../types";
+
+/**
+ * Returns the angle of a line in degrees.
+ */
+export function angle(line: [DataPoint, DataPoint]): number {
+  return (
+    Math.atan2(line[1][1] - line[0][1], line[1][0] - line[0][0]) *
+    (180 / Math.PI)
+  );
+}
+
+/**
+ * Returns the midpoint of a line.
+ */
+export function midpoint(line: [DataPoint, DataPoint]): DataPoint {
+  return [
+    (line[0][0] + line[1][0]) / 2,
+    (line[0][1] + line[1][1]) / 2
+  ];
+}
diff --git a/src/utils/interpose.js b/src/utils/interpose.js
deleted file mode 100644
index 61802c8..0000000
--- a/src/utils/interpose.js
+++ /dev/null
@@ -1,40 +0,0 @@
-import { angle, midpoint } from "./geometry";
-
-// Given a start point, an end point, and a prediciton function,
-// returns a smooth line.
-export function interpose(xmin, xmax, predict){
-  const l = Math.log(xmax - xmin) * Math.LOG10E + 1 | 0;
-  const precision = 1 * Math.pow(10, -l / 2 - 1), maxIter = 1e4;
-  let points = [px(xmin), px(xmax)], iter = 0;
-
-  while (find(points) && iter < maxIter);
-
-  return points;
-  
-  function px(x){
-    return [x, predict(x)];
-  }
-
-  function find(points){
-    iter++;
-    const n = points.length;
-    let found = false;
-    
-    for (let i = 0; i < n - 1; i++){
-      const p0 = points[i],
-          p1 = points[i + 1],
-          m = midpoint([p0, p1]),
-          mp = px(m[0]),
-          a0 = angle([p0, m]),
-          a1 = angle([p0, mp]),
-          a = Math.abs(a0 - a1);
-      
-      if (a > precision){
-        points.splice(i + 1, 0, mp);
-        found = true;
-      }
-    }
-    
-    return found;
-  }
-}
\ No newline at end of file
diff --git a/src/utils/interpose.ts b/src/utils/interpose.ts
new file mode 100644
index 0000000..41fa987
--- /dev/null
+++ b/src/utils/interpose.ts
@@ -0,0 +1,49 @@
+import { angle, midpoint } from "./geometry";
+import { PredictFunction, DataPoint } from "../types";
+
+/**
+ * Given a start point (xmin), an end point (xmax),
+ * and a prediction function, returns a smooth line.
+ */
+export function interpose(
+  xmin: number,
+  xmax: number,
+  predict: PredictFunction
+): [DataPoint, DataPoint] {
+  const l = (Math.log(xmax - xmin) * Math.LOG10E + 1) | 0;
+  const precision = Math.pow(10, -l / 2 - 1);
+  const maxIter = 1e4;
+  let points: [DataPoint, DataPoint] = [px(xmin), px(xmax)];
+  let iter = 0;
+  
+  while (find(points) && iter < maxIter) ;
+  
+  return points;
+  
+  function px(x: number): DataPoint {
+    return [x, predict(x)];
+  }
+  
+  function find(points: DataPoint[]): boolean {
+    iter++;
+    const n = points.length;
+    let found = false;
+    
+    for (let i = 0; i < n - 1; i++) {
+      const p0 = points[i];
+      const p1 = points[i + 1];
+      const m = midpoint([p0, p1]);
+      const mp = px(m[0]);
+      const a0 = angle([p0, m]);
+      const a1 = angle([p0, mp]);
+      const a = Math.abs(a0 - a1);
+      
+      if (a > precision) {
+        points.splice(i + 1, 0, mp);
+        found = true;
+      }
+    }
+    
+    return found;
+  }
+}
diff --git a/src/utils/median.js b/src/utils/median.ts
similarity index 52%
rename from src/utils/median.js
rename to src/utils/median.ts
index d040c87..f823d9a 100644
--- a/src/utils/median.js
+++ b/src/utils/median.ts
@@ -1,6 +1,8 @@
-// Returns the medium value of an array of numbers.
-export function median(arr){
+/**
+ * Returns the median value of an array of numbers.
+ */
+export function median(arr: Float64Array): number {
   arr.sort((a, b) => a - b);
   var i = arr.length / 2;
   return i % 1 === 0 ? (arr[i - 1] + arr[i]) / 2 : arr[Math.floor(i)];
-}
\ No newline at end of file
+}
diff --git a/src/utils/ols.js b/src/utils/ols.js
deleted file mode 100644
index f621548..0000000
--- a/src/utils/ols.js
+++ /dev/null
@@ -1,10 +0,0 @@
-// Ordinary Least Squares from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
-export function ols(uX, uY, uXY, uX2) {
-  const delta = uX2 - uX * uX,
-        slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta,
-        intercept = uY - slope * uX;
-
-  return [intercept, slope];
-}
\ No newline at end of file
diff --git a/src/utils/ols.ts b/src/utils/ols.ts
new file mode 100644
index 0000000..8c43c41
--- /dev/null
+++ b/src/utils/ols.ts
@@ -0,0 +1,18 @@
+/**
+ * Ordinary Least Squares from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/ols.js
+ */
+
+export function ols(
+  uX: number,
+  uY: number,
+  uXY: number,
+  uX2: number
+): [number, number] {
+  const delta = uX2 - uX * uX,
+    slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta,
+    intercept = uY - slope * uX;
+  
+  return [intercept, slope];
+}
diff --git a/src/utils/points.js b/src/utils/points.js
deleted file mode 100644
index 5e1a5c8..0000000
--- a/src/utils/points.js
+++ /dev/null
@@ -1,51 +0,0 @@
-// Adapted from vega-statistics by Jeffrey Heer
-// License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
-// Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
-export function points(data, x, y, sort) {
-  data = data.filter((d, i) => {
-    let u = x(d, i), v = y(d, i);
-    return u != null && isFinite(u) && v != null && isFinite(v);
-  });
-
-  if (sort) {
-    data.sort((a, b) => x(a) - x(b));
-  }
-
-  const n = data.length,
-        X = new Float64Array(n),
-        Y = new Float64Array(n);
-
-  // extract values, calculate means
-  let ux = 0, uy = 0, xv, yv, d;
-  for (let i = 0; i < n; ) {
-    d = data[i];
-    X[i] = xv = +x(d, i, data);
-    Y[i] = yv = +y(d, i, data);
-    ++i;
-    ux += (xv - ux) / i;
-    uy += (yv - uy) / i;
-  }
-
-  // mean center the data
-  for (let i = 0; i < n; ++i) {
-    X[i] -= ux;
-    Y[i] -= uy;
-  }
-
-  return [X, Y, ux, uy];
-}
-
-
-export function visitPoints(data, x, y, cb){
-  let iterations = 0;
-
-  for (let i = 0, n = data.length; i < n; i++) {
-    const d = data[i],
-          dx = +x(d, i, data),
-          dy = +y(d, i, data);
-
-    if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
-      cb(dx, dy, iterations++);
-    }
-  }
-}
diff --git a/src/utils/points.ts b/src/utils/points.ts
new file mode 100644
index 0000000..944377e
--- /dev/null
+++ b/src/utils/points.ts
@@ -0,0 +1,67 @@
+import { Accessor } from "../types";
+
+/**
+ * Adapted from vega-statistics by Jeffrey Heer
+ * License: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/LICENSE
+ * Source: https://github.com/vega/vega/blob/f058b099decad9db78301405dd0d2e9d8ba3d51a/packages/vega-statistics/src/regression/points.js
+ */
+export function points<T>(
+  data: T[],
+  x: Accessor<T>,
+  y: Accessor<T>,
+  sort?: boolean
+): [Float64Array, Float64Array, number, number] {
+  data = data.filter((d, i) => {
+    let u = x(d, i), v = y(d, i);
+    return u != null && isFinite(u) && v != null && isFinite(v);
+  });
+  
+  
+  if (sort) {
+    data.sort((a, b) => x(a) - x(b));
+  }
+  
+  
+  const n = data.length,
+    X = new Float64Array(n),
+    Y = new Float64Array(n);
+  
+  // extract values, calculate means
+  let ux = 0, uy = 0, xv, yv, d;
+  for (let i = 0; i < n;) {
+    d = data[i];
+    X[i] = xv = +x(d, i, data);
+    Y[i] = yv = +y(d, i, data);
+    ++i;
+    ux += (xv - ux) / i;
+    uy += (yv - uy) / i;
+  }
+  
+  // mean center the data
+  for (let i = 0; i < n; ++i) {
+    X[i] -= ux;
+    Y[i] -= uy;
+  }
+  
+  return [X, Y, ux, uy];
+}
+
+/**
+ * Iterates over valid data points, invoking a callback for each.
+ */
+export function visitPoints<T>(
+  data: T[],
+  x: Accessor<T>,
+  y: Accessor<T>,
+  cb: (dx: number, dy: number, index: number) => void
+): void {
+  let iterations = 0;
+  for (let i = 0; i < data.length; i++) {
+    const d = data[i];
+    const dx = +x(d, i, data);
+    const dy = +y(d, i, data);
+    if (dx != null && isFinite(dx) && dy != null && isFinite(dy)) {
+      cb(dx, dy, iterations++);
+    }
+  }
+}
diff --git a/test/loess-test.js b/test/loess-test.js
index 0f163f3..13c965c 100644
--- a/test/loess-test.js
+++ b/test/loess-test.js
@@ -27,4 +27,4 @@ tape("loess(data) calculates the LOESS regression", function(test) {
   
   test.deepEqual(r, output);
   test.end();
-});
\ No newline at end of file
+});
diff --git a/tsconfig.json b/tsconfig.json
new file mode 100644
index 0000000..2ac344c
--- /dev/null
+++ b/tsconfig.json
@@ -0,0 +1,20 @@
+{
+  "compilerOptions": {
+    "target": "ES5",
+    "module": "ESNext",
+    "declaration": true,
+    "declarationDir": "./dist/types",
+    "outDir": "./dist",
+    "allowJs": true,
+    "strict": true
+  },
+  "include": [
+    "index.ts",
+    "src/**/*.ts"
+  ],
+  "exclude": [
+    "node_modules",
+    "dist",
+    "**/*.test.js"
+  ]
+}