diff --git a/stat/roc.go b/stat/roc.go
index 952361e1..4e90a887 100644
--- a/stat/roc.go
+++ b/stat/roc.go
@@ -127,3 +127,74 @@ func ROC(cutoffs, y []float64, classes []bool, weights []float64) (tpr, fpr, thr
 
 	return tpr, fpr, cutoffs
 }
+
+// TOC returns the Total Operating Characteristic for the classes provided
+// and the minimum and maximum bounds for the TOC.
+//
+// The input y values that correspond to classes and weights must be sorted
+// in ascending order. classes[i] is the class of value y[i] and weights[i]
+// is the weight of y[i]. SortWeightedLabeled can be used to sort classes
+// together with weights by the rank variable, i+1.
+//
+// The returned ntp values can be interpreted as the number of true positives
+// where values above the given rank are assigned class true for each given
+// rank from 1 to len(classes).
+//  ntp_i = sum_{j = 1}^{len(ntp)-i-1} [ classes_j ] * weights_j, where [x] = 1 if x else 0.
+// and
+//  ntp_0 = 0
+// The values of min and max provide the minimum and maximum possible number
+// of false values for the set of classes. The first element of ntp, min and
+// max are always zero as this corresponds to assigning all data class false
+// and the last elements are always weighted sum of classes as this corresponds
+// to assigning every data class true.
+//
+// If weights is nil, all weights are treated as 1. When weights are not nil,
+// the calculation of min and max allows for partial assignment of single data
+// points. If weights is not nil it must have the same length as classes,
+// otherwise TOC will panic.
+//
+// More details about TOC curves are available at
+// https://en.wikipedia.org/wiki/Total_operating_characteristic
+func TOC(classes []bool, weights []float64) (min, ntp, max []float64) {
+	if weights != nil && len(classes) != len(weights) {
+		panic("stat: slice length mismatch")
+	}
+	if len(classes) == 0 {
+		return nil, nil, nil
+	}
+
+	ntp = make([]float64, len(classes)+1)
+	min = make([]float64, len(ntp))
+	max = make([]float64, len(ntp))
+	if weights == nil {
+		for i := range ntp[1:] {
+			ntp[i+1] = ntp[i]
+			if classes[len(classes)-i-1] {
+				ntp[i+1]++
+			}
+		}
+		totalPositive := ntp[len(ntp)-1]
+		for i := range ntp {
+			min[i] = math.Max(0, totalPositive-float64(len(classes)-i))
+			max[i] = math.Min(totalPositive, float64(i))
+		}
+		return min, ntp, max
+	}
+
+	cumw := max // Reuse max for cumulative weight. Update its elements last.
+	for i := range ntp[1:] {
+		ntp[i+1] = ntp[i]
+		w := weights[len(weights)-i-1]
+		cumw[i+1] = cumw[i] + w
+		if classes[len(classes)-i-1] {
+			ntp[i+1] += w
+		}
+	}
+	totw := cumw[len(cumw)-1]
+	totalPositive := ntp[len(ntp)-1]
+	for i := range ntp {
+		min[i] = math.Max(0, totalPositive-(totw-cumw[i]))
+		max[i] = math.Min(totalPositive, cumw[i])
+	}
+	return min, ntp, max
+}
diff --git a/stat/roc_example_test.go b/stat/roc_example_test.go
index 011f1093..7e3e4de5 100644
--- a/stat/roc_example_test.go
+++ b/stat/roc_example_test.go
@@ -108,7 +108,7 @@ func ExampleROC_equallySpacedCutoffs() {
 	// false positive rate: [0 0 0 1 1 1 1 1 1]
 }
 
-func ExampleROC_aUC() {
+func ExampleROC_aUC_unweighted() {
 	y := []float64{0.1, 0.35, 0.4, 0.8}
 	classes := []bool{true, false, true, false}
 
@@ -125,3 +125,126 @@ func ExampleROC_aUC() {
 	// false positive rate: [0 0.5 0.5 1 1]
 	// auc: 0.25
 }
+
+func ExampleROC_aUC_weighted() {
+	y := []float64{0.1, 0.35, 0.4, 0.8}
+	classes := []bool{true, false, true, false}
+	weights := []float64{1, 2, 2, 1}
+
+	tpr, fpr, _ := stat.ROC(nil, y, classes, weights)
+
+	// Compute Area Under Curve.
+	auc := integrate.Trapezoidal(fpr, tpr)
+	fmt.Printf("auc: %f\n", auc)
+
+	// Output:
+	// auc: 0.444444
+
+}
+
+func ExampleTOC() {
+	classes := []bool{
+		false, false, false, false, false, false,
+		false, false, false, false, false, false,
+		false, false, true, true, true, true,
+		true, true, true, false, false, true,
+		false, true, false, false, true, false,
+	}
+
+	min, ntp, max := stat.TOC(classes, nil)
+	fmt.Printf("minimum bound: %v\n", min)
+	fmt.Printf("TOC:           %v\n", ntp)
+	fmt.Printf("maximum bound: %v\n", max)
+
+	// Output:
+	// minimum bound: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 10]
+	// TOC:           [0 0 1 1 1 2 2 3 3 3 4 5 6 7 8 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10]
+	// maximum bound: [0 1 2 3 4 5 6 7 8 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10]
+}
+
+func ExampleTOC_unsorted() {
+	y := []float64{8, 7.5, 6, 5, 3, 0}
+	classes := []bool{true, false, true, false, false, false}
+	weights := []float64{4, 1, 6, 3, 2, 2}
+
+	stat.SortWeightedLabeled(y, classes, weights)
+
+	min, ntp, max := stat.TOC(classes, weights)
+	fmt.Printf("minimum bound: %v\n", min)
+	fmt.Printf("TOC:           %v\n", ntp)
+	fmt.Printf("maximum bound: %v\n", max)
+
+	// Output:
+	// minimum bound: [0 0 0 3 6 8 10]
+	// TOC:           [0 4 4 10 10 10 10]
+	// maximum bound: [0 4 5 10 10 10 10]
+}
+
+func ExampleTOC_aUC_unweighted() {
+	classes := []bool{true, false, true, false}
+
+	_, ntp, _ := stat.TOC(classes, nil)
+	pos := ntp[len(ntp)-1]
+	base := float64(len(classes)) - pos
+
+	// Compute the area under ntp and under the
+	// minimum bound.
+	x := floats.Span(make([]float64, len(classes)+1), 0, float64(len(classes)))
+	aucNTP := integrate.Trapezoidal(x, ntp)
+	aucMin := pos * pos / 2
+
+	// Calculate the the area under the curve
+	// within the bounding parallelogram.
+	auc := aucNTP - aucMin
+
+	// Calculate the area within the bounding
+	// parallelogram.
+	par := pos * base
+
+	// The AUC is the ratio of the area under
+	// the curve within the bounding parallelogram
+	// and the total parallelogram bound.
+	auc /= par
+
+	fmt.Printf("number of true positives: %v\n", ntp)
+	fmt.Printf("auc: %v\n", auc)
+
+	// Output:
+	// number of true positives: [0 0 1 1 2]
+	// auc: 0.25
+}
+
+func ExampleTOC_aUC_weighted() {
+	classes := []bool{true, false, true, false}
+	weights := []float64{1, 2, 2, 1}
+
+	min, ntp, max := stat.TOC(classes, weights)
+
+	// Compute the area under ntp and under the
+	// minimum and maximum bounds.
+	x := make([]float64, len(classes)+1)
+	floats.CumSum(x[1:], weights)
+	aucNTP := integrate.Trapezoidal(x, ntp)
+	aucMin := integrate.Trapezoidal(x, min)
+	aucMax := integrate.Trapezoidal(x, max)
+
+	// Calculate the the area under the curve
+	// within the bounding parallelogram.
+	auc := aucNTP - aucMin
+
+	// Calculate the area within the bounding
+	// parallelogram.
+	par := aucMax - aucMin
+
+	// The AUC is the ratio of the area under
+	// the curve within the bounding parallelogram
+	// and the total parallelogram bound.
+	auc /= par
+
+	fmt.Printf("number of true positives: %v\n", ntp)
+	fmt.Printf("auc: %f\n", auc)
+
+	// Output:
+	// number of true positives: [0 0 2 2 3]
+	// auc: 0.444444
+}
diff --git a/stat/roc_test.go b/stat/roc_test.go
index 1e286b56..e0adea3a 100644
--- a/stat/roc_test.go
+++ b/stat/roc_test.go
@@ -256,3 +256,71 @@ func TestROC(t *testing.T) {
 		}
 	}
 }
+
+func TestTOC(t *testing.T) {
+	cases := []struct {
+		c       []bool
+		w       []float64
+		wantMin []float64
+		wantMax []float64
+		wantTOC []float64
+	}{
+		{ // 0
+			// This is the example given in the paper's supplement.
+			// http://www2.clarku.edu/~rpontius/TOCexample2.xlsx
+			// It is also shown in the WP article.
+			// https://en.wikipedia.org/wiki/Total_operating_characteristic#/media/File:TOC_labeled.png
+			c: []bool{
+				false, false, false, false, false, false,
+				false, false, false, false, false, false,
+				false, false, true, true, true, true,
+				true, true, true, false, false, true,
+				false, true, false, false, true, false,
+			},
+			wantMin: []float64{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+			wantMax: []float64{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10},
+			wantTOC: []float64{0, 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10},
+		},
+		{ // 1
+			c:       []bool{},
+			wantMin: nil,
+			wantMax: nil,
+			wantTOC: nil,
+		},
+		{ // 2
+			c: []bool{
+				true, true, true, true, true,
+			},
+			wantMin: []float64{0, 1, 2, 3, 4, 5},
+			wantMax: []float64{0, 1, 2, 3, 4, 5},
+			wantTOC: []float64{0, 1, 2, 3, 4, 5},
+		},
+		{ // 3
+			c: []bool{
+				false, false, false, false, false,
+			},
+			wantMin: []float64{0, 0, 0, 0, 0, 0},
+			wantMax: []float64{0, 0, 0, 0, 0, 0},
+			wantTOC: []float64{0, 0, 0, 0, 0, 0},
+		},
+		{ // 4
+			c:       []bool{false, false, false, true, false, true},
+			w:       []float64{2, 2, 3, 6, 1, 4},
+			wantMin: []float64{0, 0, 0, 3, 6, 8, 10},
+			wantMax: []float64{0, 4, 5, 10, 10, 10, 10},
+			wantTOC: []float64{0, 4, 4, 10, 10, 10, 10},
+		},
+	}
+	for i, test := range cases {
+		gotMin, gotTOC, gotMax := TOC(test.c, test.w)
+		if !floats.Same(gotMin, test.wantMin) {
+			t.Errorf("%d: unexpected minimum bound got:%v want:%v", i, gotMin, test.wantMin)
+		}
+		if !floats.Same(gotMax, test.wantMax) {
+			t.Errorf("%d: unexpected maximum bound got:%v want:%v", i, gotMax, test.wantMax)
+		}
+		if !floats.Same(gotTOC, test.wantTOC) {
+			t.Errorf("%d: unexpected TOC got:%v want:%v", i, gotTOC, test.wantTOC)
+		}
+	}
+}