diff --git a/docs/VECTOR.api.md b/docs/VECTOR.api.md
index 01aa6f5..f524497 100644
--- a/docs/VECTOR.api.md
+++ b/docs/VECTOR.api.md
@@ -11,7 +11,7 @@ export type ApproximationFunction<S> = (input: Vector<S>) => S;
 export type ApproximationFunctionTemplate<S> = (coefficients: Vector<S>) => ApproximationFunction<S>;
 
 // @public
-export abstract class ArrayMatrix<S> implements Matrix<S> {
+export abstract class ArrayMatrix<S = number> implements Matrix<S> {
     // @internal
     protected constructor(data: MatrixData<S>);
     add(other: Matrix<S>): Matrix<S>;
@@ -43,7 +43,7 @@ export abstract class ArrayMatrix<S> implements Matrix<S> {
 }
 
 // @public
-export abstract class ArrayVector<S> implements Vector<S> {
+export abstract class ArrayVector<S = number> implements Vector<S> {
     // @internal
     protected constructor(data: VectorData<S>);
     add(other: Vector<S>): Vector<S>;
@@ -107,9 +107,9 @@ export interface CholeskyDecomposition<S> {
 // @public
 export interface Classifier<H> {
     getHyperParameters(): H;
-    predict(data: Matrix<number>, pThreshold: number): Vector<number>;
-    predictProbabilities(data: Matrix<number>): Vector<number>;
-    train(data: Matrix<number>, target: Vector<number>): void;
+    predict(data: Matrix, pThreshold: number): Vector;
+    predictProbabilities(data: Matrix): Vector;
+    train(data: Matrix, target: Vector): void;
 }
 
 // @public
@@ -202,11 +202,11 @@ export interface Cost {
     // (undocumented)
     cost: number;
     // (undocumented)
-    gradient: Vector<number>;
+    gradient: Vector;
 }
 
 // @public
-export type CostFunction = (theta: Vector<number>) => Cost;
+export type CostFunction = (theta: Vector) => Cost;
 
 // @public
 export function covariance<S>(first: Vector<S>, second: Vector<S>): S;
@@ -218,13 +218,13 @@ export function covariance<S>(A: Matrix<S>): Matrix<S>;
 export function crossProduct<S>(first: Vector<S>, second: Vector<S>): Vector<S>;
 
 // @public
-export function derivative(f: (x: number) => number, xMin: number, xMax: number, binCount: number): Vector<number>;
+export function derivative(f: (x: number) => number, xMin: number, xMax: number, binCount: number): Vector;
 
 // @public
 export function determinant<S>(matrix: Matrix<S>): S;
 
 // @public
-export function diag(elements: number[]): Matrix<number>;
+export function diag(elements: number[]): Matrix;
 
 // Warning: (ae-forgotten-export) The symbol "DimensionReductionType" needs to be exported by the entry point index.d.ts
 //
@@ -254,7 +254,7 @@ export function euclideanNorm<S>(v: Vector<S>): number;
 export function exp<S>(A: Matrix<S>, order?: number): Matrix<S>;
 
 // @public
-export function eye(size: number): Matrix<number>;
+export function eye(size: number): Matrix;
 
 // @public
 export class FloatMatrix implements Matrix<number> {
@@ -382,13 +382,13 @@ export function isSymmetric<S>(matrix: Matrix<S>): boolean;
 export function isUpperTriangular<S>(matrix: Matrix<S>): boolean;
 
 // @public
-export type Kernel = (data: Matrix<number>, trainingData?: Matrix<number>) => Matrix<number>;
+export type Kernel = (data: Matrix, trainingData?: Matrix) => Matrix;
 
 // @public
 export function kroneckerProduct<S>(first: Matrix<S>, second: Matrix<S>): Matrix<S>;
 
 // @public
-export type LearningAlgorithm = (initialTheta: Vector<number>, costFn: CostFunction) => Vector<number>;
+export type LearningAlgorithm = (initialTheta: Vector, costFn: CostFunction) => Vector;
 
 // @public
 export interface LeastSquaresApproximation<S> {
@@ -397,15 +397,15 @@ export interface LeastSquaresApproximation<S> {
 }
 
 // @public
-export function LinearKernel(data: Matrix<number>): Matrix<number>;
+export function LinearKernel(data: Matrix): Matrix;
 
 // @public
 export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
     constructor(hyperParameters: Partial<LinearRegressorHyperparams>);
     getHyperParameters(): LinearRegressorHyperparams;
-    getParameters(): Vector<number> | undefined;
-    predict(data: Matrix<number>): Vector<number>;
-    train(data: Matrix<number>, target: Vector<number>): void;
+    getParameters(): Vector | undefined;
+    predict(data: Matrix): Vector;
+    train(data: Matrix, target: Vector): void;
 }
 
 // @public
@@ -425,10 +425,10 @@ export function linspace(xMin: number, xMax: number, binCount: number): NumberVe
 export class LogisticRegressionClassifier implements Classifier<LogisticRegressionHyperparams> {
     constructor(hyperParameters: Partial<LogisticRegressionHyperparams>);
     getHyperParameters(): LogisticRegressionHyperparams;
-    getParameters(): Vector<number> | undefined;
-    predict(data: Matrix<number>): Vector<number>;
-    predictProbabilities(data: Matrix<number>): Vector<number>;
-    train(data: Matrix<number>, target: Vector<number>): void;
+    getParameters(): Vector | undefined;
+    predict(data: Matrix): Vector;
+    predictProbabilities(data: Matrix): Vector;
+    train(data: Matrix, target: Vector): void;
 }
 
 // @public
@@ -447,10 +447,10 @@ export interface LUDecomposition<S> {
 }
 
 // @public
-export function mat(data: number[][]): Matrix<number>;
+export function mat(data: number[][]): Matrix;
 
 // @public
-export interface Matrix<S> extends LinearTransformation<Vector<S>, Vector<S>> {
+export interface Matrix<S = number> extends LinearTransformation<Vector<S>, Vector<S>> {
     add(other: Matrix<S>): Matrix<S>;
     adjoint(): Matrix<S>;
     apply(vector: Vector<S>): Vector<S>;
@@ -597,10 +597,10 @@ export class NumberVector extends ArrayVector<number> {
 }
 
 // @public
-export function ones(entries: number): Vector<number>;
+export function ones(entries: number): Vector;
 
 // @public
-export function ones(shape: MatrixShape): Matrix<number>;
+export function ones(shape: MatrixShape): Matrix;
 
 // @public
 export function pca<S>(A: Matrix<S>, useCorrelation?: boolean): PrincipalComponentAnalysis<S>;
@@ -644,7 +644,7 @@ export function RadialBasisFunction(distanceMetric: SimilarityMetric): Kernel;
 export function rank<S>(matrix: Matrix<S>): number;
 
 // @public
-export function reduceDimensions(A: Matrix<number>, options: DimensionReductionOptions): Matrix<number>;
+export function reduceDimensions(A: Matrix, options: DimensionReductionOptions): Matrix;
 
 // @public
 export function reducedRowEchelonForm<S>(matrix: Matrix<S>): Matrix<S>;
@@ -652,8 +652,8 @@ export function reducedRowEchelonForm<S>(matrix: Matrix<S>): Matrix<S>;
 // @public
 export interface Regressor<H> {
     getHyperParameters(): H;
-    predict(data: Matrix<number>): Vector<number>;
-    train(data: Matrix<number>, target: Vector<number>): void;
+    predict(data: Matrix): Vector;
+    train(data: Matrix, target: Vector): void;
 }
 
 // @public
@@ -704,7 +704,7 @@ export abstract class ScalarOperations<S> {
 }
 
 // @public
-export type SimilarityMetric = (v1: Vector<number>, v2: Vector<number>) => number;
+export type SimilarityMetric = (v1: Vector, v2: Vector) => number;
 
 // @public
 export interface SingularValueDecomposition<S> {
@@ -725,7 +725,7 @@ export function solveByGaussianElimination<S>(A: Matrix<S>, b: Vector<S>): Linea
 export function solveOverdeterminedSystem<S>(A: Matrix<S>, b: Vector<S>): Vector<S>;
 
 // @public
-export abstract class SparseMatrix<S> implements Matrix<S> {
+export abstract class SparseMatrix<S = number> implements Matrix<S> {
     // @internal
     protected constructor(data: MatrixData<S>);
     add(other: Matrix<S>): Matrix<S>;
@@ -791,7 +791,7 @@ export class SparseNumberVector extends SparseVector<number> {
 }
 
 // @public
-export abstract class SparseVector<S> implements Vector<S> {
+export abstract class SparseVector<S = number> implements Vector<S> {
     // @internal
     protected constructor(data: VectorData<S>);
     add(other: Vector<S>): Vector<S>;
@@ -838,10 +838,10 @@ export function sumNorm<S>(v: Vector<S>): number;
 export class SupportVectorMachineClassifier implements Classifier<SupportVectorMachineHyperparams> {
     constructor(hyperParameters: Partial<SupportVectorMachineHyperparams>);
     getHyperParameters(): SupportVectorMachineHyperparams;
-    getParameters(): Vector<number> | undefined;
-    predict(data: Matrix<number>): Vector<number>;
-    predictProbabilities(_data: Matrix<number>): Vector<number>;
-    train(data: Matrix<number>, target: Vector<number>): void;
+    getParameters(): Vector | undefined;
+    predict(data: Matrix): Vector;
+    predictProbabilities(_data: Matrix): Vector;
+    train(data: Matrix, target: Vector): void;
     }
 
 // @public
@@ -863,10 +863,10 @@ export function variance<S>(x: Vector<S>): S;
 export function variance<S>(A: Matrix<S>): Vector<S>;
 
 // @public
-export function vec(data: number[]): Vector<number>;
+export function vec(data: number[]): Vector;
 
 // @public
-export interface Vector<S> {
+export interface Vector<S = number> {
     add(other: Vector<S>): Vector<S>;
     builder(): VectorBuilder<S, Vector<S>>;
     combine(other: Vector<S>, combineEntries: (a: S, b: S) => S): Vector<S>;
@@ -929,10 +929,10 @@ export type VectorData<S> = readonly S[];
 export type VectorIndexFunction<S> = (index: number) => S;
 
 // @public
-export function zeros(entries: number): Vector<number>;
+export function zeros(entries: number): Vector;
 
 // @public
-export function zeros(shape: MatrixShape): Matrix<number>;
+export function zeros(shape: MatrixShape): Matrix;
 
 
 // (No @packageDocumentation comment for this package)
diff --git a/docs/api/vector.arraymatrix.md b/docs/api/vector.arraymatrix.md
index 689e5f4..f7aa3a0 100644
--- a/docs/api/vector.arraymatrix.md
+++ b/docs/api/vector.arraymatrix.md
@@ -9,7 +9,7 @@ Implements [Matrix](./vector.matrix.md) with a 2-dimensional array of values.
 <b>Signature:</b>
 
 ```typescript
-export declare abstract class ArrayMatrix<S> implements Matrix<S> 
+export declare abstract class ArrayMatrix<S = number> implements Matrix<S> 
 ```
 
 ## Remarks
diff --git a/docs/api/vector.arrayvector.md b/docs/api/vector.arrayvector.md
index f2ada3c..76defc5 100644
--- a/docs/api/vector.arrayvector.md
+++ b/docs/api/vector.arrayvector.md
@@ -9,7 +9,7 @@ Implements [Vector](./vector.vector.md) with an array of values.
 <b>Signature:</b>
 
 ```typescript
-export declare abstract class ArrayVector<S> implements Vector<S> 
+export declare abstract class ArrayVector<S = number> implements Vector<S> 
 ```
 
 ## Remarks
diff --git a/docs/api/vector.classifier.predict.md b/docs/api/vector.classifier.predict.md
index 24f2ab4..5df1a2b 100644
--- a/docs/api/vector.classifier.predict.md
+++ b/docs/api/vector.classifier.predict.md
@@ -9,19 +9,19 @@ Uses the learned parameters to make predictions based on a set of input data.
 <b>Signature:</b>
 
 ```typescript
-predict(data: Matrix<number>, pThreshold: number): Vector<number>;
+predict(data: Matrix, pThreshold: number): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 |  pThreshold | <code>number</code> | The probability threshold above which an event will be predicted |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.classifier.predictprobabilities.md b/docs/api/vector.classifier.predictprobabilities.md
index 6875e20..579789d 100644
--- a/docs/api/vector.classifier.predictprobabilities.md
+++ b/docs/api/vector.classifier.predictprobabilities.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions for the probability of an event
 <b>Signature:</b>
 
 ```typescript
-predictProbabilities(data: Matrix<number>): Vector<number>;
+predictProbabilities(data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.classifier.train.md b/docs/api/vector.classifier.train.md
index 913be6b..7e17c2c 100644
--- a/docs/api/vector.classifier.train.md
+++ b/docs/api/vector.classifier.train.md
@@ -9,15 +9,15 @@ Learns the optimal set of parameters for the model.
 <b>Signature:</b>
 
 ```typescript
-train(data: Matrix<number>, target: Vector<number>): void;
+train(data: Matrix, target: Vector): void;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
-|  target | <code>Vector&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
+|  data | <code>Matrix</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
+|  target | <code>Vector</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
 
 <b>Returns:</b>
 
diff --git a/docs/api/vector.cost.gradient.md b/docs/api/vector.cost.gradient.md
index 8b5b054..6e006b2 100644
--- a/docs/api/vector.cost.gradient.md
+++ b/docs/api/vector.cost.gradient.md
@@ -7,5 +7,5 @@
 <b>Signature:</b>
 
 ```typescript
-gradient: Vector<number>;
+gradient: Vector;
 ```
diff --git a/docs/api/vector.cost.md b/docs/api/vector.cost.md
index 4fd54cf..12138ba 100644
--- a/docs/api/vector.cost.md
+++ b/docs/api/vector.cost.md
@@ -17,5 +17,5 @@ export interface Cost
 |  Property | Type | Description |
 |  --- | --- | --- |
 |  [cost](./vector.cost.cost.md) | <code>number</code> |  |
-|  [gradient](./vector.cost.gradient.md) | <code>Vector&lt;number&gt;</code> |  |
+|  [gradient](./vector.cost.gradient.md) | <code>Vector</code> |  |
 
diff --git a/docs/api/vector.costfunction.md b/docs/api/vector.costfunction.md
index 957c4dd..07592ff 100644
--- a/docs/api/vector.costfunction.md
+++ b/docs/api/vector.costfunction.md
@@ -9,5 +9,5 @@ A function that evaluates the cost of a set of parameters `theta`
 <b>Signature:</b>
 
 ```typescript
-export declare type CostFunction = (theta: Vector<number>) => Cost;
+export declare type CostFunction = (theta: Vector) => Cost;
 ```
diff --git a/docs/api/vector.derivative.md b/docs/api/vector.derivative.md
index ecce052..354e77b 100644
--- a/docs/api/vector.derivative.md
+++ b/docs/api/vector.derivative.md
@@ -9,7 +9,7 @@ Uses finite differences to build a vector containing approximate values of the d
 <b>Signature:</b>
 
 ```typescript
-export declare function derivative(f: (x: number) => number, xMin: number, xMax: number, binCount: number): Vector<number>;
+export declare function derivative(f: (x: number) => number, xMin: number, xMax: number, binCount: number): Vector;
 ```
 
 ## Parameters
@@ -23,7 +23,7 @@ export declare function derivative(f: (x: number) => number, xMin: number, xMax:
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 A linearly spaced vector whose values represent the values of the derivative
 
diff --git a/docs/api/vector.diag.md b/docs/api/vector.diag.md
index 6279a73..8837655 100644
--- a/docs/api/vector.diag.md
+++ b/docs/api/vector.diag.md
@@ -9,7 +9,7 @@ Creates a new matrix with the specified entries on the diagonal. See [MatrixBuil
 <b>Signature:</b>
 
 ```typescript
-export declare function diag(elements: number[]): Matrix<number>;
+export declare function diag(elements: number[]): Matrix;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function diag(elements: number[]): Matrix<number>;
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/docs/api/vector.eye.md b/docs/api/vector.eye.md
index 8d360a3..e380b55 100644
--- a/docs/api/vector.eye.md
+++ b/docs/api/vector.eye.md
@@ -9,7 +9,7 @@ Creates a new identity matrix of size `size`<!-- -->. See [MatrixBuilder.identit
 <b>Signature:</b>
 
 ```typescript
-export declare function eye(size: number): Matrix<number>;
+export declare function eye(size: number): Matrix;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function eye(size: number): Matrix<number>;
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/docs/api/vector.kernel.md b/docs/api/vector.kernel.md
index 62241bd..ac71ae6 100644
--- a/docs/api/vector.kernel.md
+++ b/docs/api/vector.kernel.md
@@ -11,5 +11,5 @@ Generally intended for use with a [SupportVectorMachineClassifier](./vector.supp
 <b>Signature:</b>
 
 ```typescript
-export declare type Kernel = (data: Matrix<number>, trainingData?: Matrix<number>) => Matrix<number>;
+export declare type Kernel = (data: Matrix, trainingData?: Matrix) => Matrix;
 ```
diff --git a/docs/api/vector.learningalgorithm.md b/docs/api/vector.learningalgorithm.md
index d914ebe..f1bad91 100644
--- a/docs/api/vector.learningalgorithm.md
+++ b/docs/api/vector.learningalgorithm.md
@@ -9,5 +9,5 @@ An function which, given an initial value of `theta` and a CostFunction, will co
 <b>Signature:</b>
 
 ```typescript
-export declare type LearningAlgorithm = (initialTheta: Vector<number>, costFn: CostFunction) => Vector<number>;
+export declare type LearningAlgorithm = (initialTheta: Vector, costFn: CostFunction) => Vector;
 ```
diff --git a/docs/api/vector.linearkernel.md b/docs/api/vector.linearkernel.md
index abf8ed8..4a2cf02 100644
--- a/docs/api/vector.linearkernel.md
+++ b/docs/api/vector.linearkernel.md
@@ -9,16 +9,16 @@ A linear kernel for use in a [SupportVectorMachineClassifier](./vector.supportve
 <b>Signature:</b>
 
 ```typescript
-export declare function LinearKernel(data: Matrix<number>): Matrix<number>;
+export declare function LinearKernel(data: Matrix): Matrix;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | The variance of the gaussian distribution used in the kernel |
+|  data | <code>Matrix</code> | The variance of the gaussian distribution used in the kernel |
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/docs/api/vector.linearregressor.getparameters.md b/docs/api/vector.linearregressor.getparameters.md
index 8dd5f20..e886263 100644
--- a/docs/api/vector.linearregressor.getparameters.md
+++ b/docs/api/vector.linearregressor.getparameters.md
@@ -9,9 +9,9 @@ Get the coefficients of the trained linear regression model, or `undefined` if t
 <b>Signature:</b>
 
 ```typescript
-getParameters(): Vector<number> | undefined;
+getParameters(): Vector | undefined;
 ```
 <b>Returns:</b>
 
-`Vector<number> | undefined`
+`Vector | undefined`
 
diff --git a/docs/api/vector.linearregressor.predict.md b/docs/api/vector.linearregressor.predict.md
index af853a4..4882fb9 100644
--- a/docs/api/vector.linearregressor.predict.md
+++ b/docs/api/vector.linearregressor.predict.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions based on a set of input data.
 <b>Signature:</b>
 
 ```typescript
-predict(data: Matrix<number>): Vector<number>;
+predict(data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.linearregressor.train.md b/docs/api/vector.linearregressor.train.md
index 0db7717..7ef8b85 100644
--- a/docs/api/vector.linearregressor.train.md
+++ b/docs/api/vector.linearregressor.train.md
@@ -9,15 +9,15 @@ Learns the optimal set of parameters for the model.
 <b>Signature:</b>
 
 ```typescript
-train(data: Matrix<number>, target: Vector<number>): void;
+train(data: Matrix, target: Vector): void;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
-|  target | <code>Vector&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
+|  data | <code>Matrix</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
+|  target | <code>Vector</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
 
 <b>Returns:</b>
 
diff --git a/docs/api/vector.logisticregressionclassifier.getparameters.md b/docs/api/vector.logisticregressionclassifier.getparameters.md
index e073c82..90bf2cf 100644
--- a/docs/api/vector.logisticregressionclassifier.getparameters.md
+++ b/docs/api/vector.logisticregressionclassifier.getparameters.md
@@ -9,9 +9,9 @@ Get the coefficients of the trained linear regression model, or `undefined` if t
 <b>Signature:</b>
 
 ```typescript
-getParameters(): Vector<number> | undefined;
+getParameters(): Vector | undefined;
 ```
 <b>Returns:</b>
 
-`Vector<number> | undefined`
+`Vector | undefined`
 
diff --git a/docs/api/vector.logisticregressionclassifier.predict.md b/docs/api/vector.logisticregressionclassifier.predict.md
index 306ceaf..2ef3237 100644
--- a/docs/api/vector.logisticregressionclassifier.predict.md
+++ b/docs/api/vector.logisticregressionclassifier.predict.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions based on a set of input data.
 <b>Signature:</b>
 
 ```typescript
-predict(data: Matrix<number>): Vector<number>;
+predict(data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.logisticregressionclassifier.predictprobabilities.md b/docs/api/vector.logisticregressionclassifier.predictprobabilities.md
index 37e50e0..be883a5 100644
--- a/docs/api/vector.logisticregressionclassifier.predictprobabilities.md
+++ b/docs/api/vector.logisticregressionclassifier.predictprobabilities.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions for the probability of an event
 <b>Signature:</b>
 
 ```typescript
-predictProbabilities(data: Matrix<number>): Vector<number>;
+predictProbabilities(data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.logisticregressionclassifier.train.md b/docs/api/vector.logisticregressionclassifier.train.md
index 31174ea..2fbaf1e 100644
--- a/docs/api/vector.logisticregressionclassifier.train.md
+++ b/docs/api/vector.logisticregressionclassifier.train.md
@@ -9,15 +9,15 @@ Learns the optimal set of parameters for the model.
 <b>Signature:</b>
 
 ```typescript
-train(data: Matrix<number>, target: Vector<number>): void;
+train(data: Matrix, target: Vector): void;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
-|  target | <code>Vector&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
+|  data | <code>Matrix</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
+|  target | <code>Vector</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
 
 <b>Returns:</b>
 
diff --git a/docs/api/vector.mat.md b/docs/api/vector.mat.md
index 46623bd..7c47c5f 100644
--- a/docs/api/vector.mat.md
+++ b/docs/api/vector.mat.md
@@ -9,7 +9,7 @@ Creates a new [Matrix](./vector.matrix.md) of numbers. See [MatrixBuilder.fromAr
 <b>Signature:</b>
 
 ```typescript
-export declare function mat(data: number[][]): Matrix<number>;
+export declare function mat(data: number[][]): Matrix;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function mat(data: number[][]): Matrix<number>;
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/docs/api/vector.matrix.md b/docs/api/vector.matrix.md
index e01e048..afa35a8 100644
--- a/docs/api/vector.matrix.md
+++ b/docs/api/vector.matrix.md
@@ -9,7 +9,7 @@ A generalized Matrix - one of the core data types
 <b>Signature:</b>
 
 ```typescript
-export interface Matrix<S> extends LinearTransformation<Vector<S>, Vector<S>> 
+export interface Matrix<S = number> extends LinearTransformation<Vector<S>, Vector<S>> 
 ```
 
 ## Methods
diff --git a/docs/api/vector.ones.md b/docs/api/vector.ones.md
index fa16767..8d53e61 100644
--- a/docs/api/vector.ones.md
+++ b/docs/api/vector.ones.md
@@ -9,7 +9,7 @@ Creates a new vector of all 1s. See [VectorBuilder.ones()](./vector.vectorbuilde
 <b>Signature:</b>
 
 ```typescript
-export declare function ones(entries: number): Vector<number>;
+export declare function ones(entries: number): Vector;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function ones(entries: number): Vector<number>;
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
diff --git a/docs/api/vector.ones_1.md b/docs/api/vector.ones_1.md
index a728a1c..be0c787 100644
--- a/docs/api/vector.ones_1.md
+++ b/docs/api/vector.ones_1.md
@@ -9,7 +9,7 @@ Creates a new matrix of all 1s. See [MatrixBuilder.ones()](./vector.matrixbuilde
 <b>Signature:</b>
 
 ```typescript
-export declare function ones(shape: MatrixShape): Matrix<number>;
+export declare function ones(shape: MatrixShape): Matrix;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function ones(shape: MatrixShape): Matrix<number>;
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/docs/api/vector.reducedimensions.md b/docs/api/vector.reducedimensions.md
index 1cec603..9b0ee9e 100644
--- a/docs/api/vector.reducedimensions.md
+++ b/docs/api/vector.reducedimensions.md
@@ -9,17 +9,17 @@ Reduce the number of dimensions of a data matrix `A` while losing as little info
 <b>Signature:</b>
 
 ```typescript
-export declare function reduceDimensions(A: Matrix<number>, options: DimensionReductionOptions): Matrix<number>;
+export declare function reduceDimensions(A: Matrix, options: DimensionReductionOptions): Matrix;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  A | <code>Matrix&lt;number&gt;</code> | The data matrix |
+|  A | <code>Matrix</code> | The data matrix |
 |  options | <code>DimensionReductionOptions</code> | Specify how the dimension reduction should be done. <code>useCorrelation: boolean</code> - use the correlation rather than covariance matrix when conducting the PCA; <code>keep: number</code> - the desired number of dimensions; <code>remove: number</code> - the number of dimensions to remove; <code>proportionOfVariance: number</code> - the desired proportion of the total variance in the data that should be explained by the remaining columns |
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/docs/api/vector.regressor.predict.md b/docs/api/vector.regressor.predict.md
index d1bfda9..404c168 100644
--- a/docs/api/vector.regressor.predict.md
+++ b/docs/api/vector.regressor.predict.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions based on a set of input data.
 <b>Signature:</b>
 
 ```typescript
-predict(data: Matrix<number>): Vector<number>;
+predict(data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.regressor.train.md b/docs/api/vector.regressor.train.md
index 0a4d33f..71aba58 100644
--- a/docs/api/vector.regressor.train.md
+++ b/docs/api/vector.regressor.train.md
@@ -9,15 +9,15 @@ Learns the optimal set of parameters for the model.
 <b>Signature:</b>
 
 ```typescript
-train(data: Matrix<number>, target: Vector<number>): void;
+train(data: Matrix, target: Vector): void;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
-|  target | <code>Vector&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
+|  data | <code>Matrix</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
+|  target | <code>Vector</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
 
 <b>Returns:</b>
 
diff --git a/docs/api/vector.similaritymetric.md b/docs/api/vector.similaritymetric.md
index 4f2e9ea..3cb3855 100644
--- a/docs/api/vector.similaritymetric.md
+++ b/docs/api/vector.similaritymetric.md
@@ -9,5 +9,5 @@ A function which expresses the similarity of two [Vector](./vector.vector.md)<!-
 <b>Signature:</b>
 
 ```typescript
-export declare type SimilarityMetric = (v1: Vector<number>, v2: Vector<number>) => number;
+export declare type SimilarityMetric = (v1: Vector, v2: Vector) => number;
 ```
diff --git a/docs/api/vector.sparsematrix.md b/docs/api/vector.sparsematrix.md
index 374ec30..15e1d42 100644
--- a/docs/api/vector.sparsematrix.md
+++ b/docs/api/vector.sparsematrix.md
@@ -9,7 +9,7 @@ Implements [Matrix](./vector.matrix.md) with a map of indices to nonzero values.
 <b>Signature:</b>
 
 ```typescript
-export declare abstract class SparseMatrix<S> implements Matrix<S> 
+export declare abstract class SparseMatrix<S = number> implements Matrix<S> 
 ```
 
 ## Remarks
diff --git a/docs/api/vector.sparsevector.md b/docs/api/vector.sparsevector.md
index 17fc801..d8c253c 100644
--- a/docs/api/vector.sparsevector.md
+++ b/docs/api/vector.sparsevector.md
@@ -9,7 +9,7 @@ Implements [Vector](./vector.vector.md) as a map of indices to nonzero values.
 <b>Signature:</b>
 
 ```typescript
-export declare abstract class SparseVector<S> implements Vector<S> 
+export declare abstract class SparseVector<S = number> implements Vector<S> 
 ```
 
 ## Remarks
diff --git a/docs/api/vector.supportvectormachineclassifier.getparameters.md b/docs/api/vector.supportvectormachineclassifier.getparameters.md
index 3944860..2863e12 100644
--- a/docs/api/vector.supportvectormachineclassifier.getparameters.md
+++ b/docs/api/vector.supportvectormachineclassifier.getparameters.md
@@ -9,9 +9,9 @@ Get the weights of the trained SVM, or `undefined` if the model has not been tra
 <b>Signature:</b>
 
 ```typescript
-getParameters(): Vector<number> | undefined;
+getParameters(): Vector | undefined;
 ```
 <b>Returns:</b>
 
-`Vector<number> | undefined`
+`Vector | undefined`
 
diff --git a/docs/api/vector.supportvectormachineclassifier.predict.md b/docs/api/vector.supportvectormachineclassifier.predict.md
index 73b3756..ee17dc9 100644
--- a/docs/api/vector.supportvectormachineclassifier.predict.md
+++ b/docs/api/vector.supportvectormachineclassifier.predict.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions based on a set of input data.
 <b>Signature:</b>
 
 ```typescript
-predict(data: Matrix<number>): Vector<number>;
+predict(data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
+|  data | <code>Matrix</code> | A [Vector](./vector.vector.md) whose rows are the observations in the test set |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.supportvectormachineclassifier.predictprobabilities.md b/docs/api/vector.supportvectormachineclassifier.predictprobabilities.md
index 2d1a102..4d924db 100644
--- a/docs/api/vector.supportvectormachineclassifier.predictprobabilities.md
+++ b/docs/api/vector.supportvectormachineclassifier.predictprobabilities.md
@@ -9,18 +9,18 @@ Uses the learned parameters to make predictions for the probability of an event
 <b>Signature:</b>
 
 ```typescript
-predictProbabilities(_data: Matrix<number>): Vector<number>;
+predictProbabilities(_data: Matrix): Vector;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  \_data | <code>Matrix&lt;number&gt;</code> |  |
+|  \_data | <code>Matrix</code> |  |
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
 ## Remarks
 
diff --git a/docs/api/vector.supportvectormachineclassifier.train.md b/docs/api/vector.supportvectormachineclassifier.train.md
index 626bd95..3dd2a7d 100644
--- a/docs/api/vector.supportvectormachineclassifier.train.md
+++ b/docs/api/vector.supportvectormachineclassifier.train.md
@@ -9,15 +9,15 @@ Learns the optimal set of parameters for the model.
 <b>Signature:</b>
 
 ```typescript
-train(data: Matrix<number>, target: Vector<number>): void;
+train(data: Matrix, target: Vector): void;
 ```
 
 ## Parameters
 
 |  Parameter | Type | Description |
 |  --- | --- | --- |
-|  data | <code>Matrix&lt;number&gt;</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
-|  target | <code>Vector&lt;number&gt;</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
+|  data | <code>Matrix</code> | A [Matrix](./vector.matrix.md) whose rows are the individual observations in the training set |
+|  target | <code>Vector</code> | A [Vector](./vector.vector.md) whose entries are the target values in the training set |
 
 <b>Returns:</b>
 
diff --git a/docs/api/vector.vec.md b/docs/api/vector.vec.md
index 0993ee7..44f3808 100644
--- a/docs/api/vector.vec.md
+++ b/docs/api/vector.vec.md
@@ -9,7 +9,7 @@ Creates a new [Vector](./vector.vector.md) of numbers. See [VectorBuilder.fromAr
 <b>Signature:</b>
 
 ```typescript
-export declare function vec(data: number[]): Vector<number>;
+export declare function vec(data: number[]): Vector;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function vec(data: number[]): Vector<number>;
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
diff --git a/docs/api/vector.vector.md b/docs/api/vector.vector.md
index 011b762..b7e9d77 100644
--- a/docs/api/vector.vector.md
+++ b/docs/api/vector.vector.md
@@ -9,7 +9,7 @@ A generalized Vector - one of the core data types
 <b>Signature:</b>
 
 ```typescript
-export interface Vector<S> 
+export interface Vector<S = number> 
 ```
 
 ## Methods
diff --git a/docs/api/vector.zeros.md b/docs/api/vector.zeros.md
index 75b7299..17c7c5a 100644
--- a/docs/api/vector.zeros.md
+++ b/docs/api/vector.zeros.md
@@ -9,7 +9,7 @@ Creates a new vector of all 0s. See [VectorBuilder.zeros()](./vector.vectorbuild
 <b>Signature:</b>
 
 ```typescript
-export declare function zeros(entries: number): Vector<number>;
+export declare function zeros(entries: number): Vector;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function zeros(entries: number): Vector<number>;
 
 <b>Returns:</b>
 
-`Vector<number>`
+`Vector`
 
diff --git a/docs/api/vector.zeros_1.md b/docs/api/vector.zeros_1.md
index 548a502..0535b9e 100644
--- a/docs/api/vector.zeros_1.md
+++ b/docs/api/vector.zeros_1.md
@@ -9,7 +9,7 @@ Creates a new matrix of all 0s. See [MatrixBuilder.zeros()](./vector.matrixbuild
 <b>Signature:</b>
 
 ```typescript
-export declare function zeros(shape: MatrixShape): Matrix<number>;
+export declare function zeros(shape: MatrixShape): Matrix;
 ```
 
 ## Parameters
@@ -20,5 +20,5 @@ export declare function zeros(shape: MatrixShape): Matrix<number>;
 
 <b>Returns:</b>
 
-`Matrix<number>`
+`Matrix`
 
diff --git a/src/applications/calculus/FiniteDifferences.ts b/src/applications/calculus/FiniteDifferences.ts
index 1b8d4e4..4dbb643 100644
--- a/src/applications/calculus/FiniteDifferences.ts
+++ b/src/applications/calculus/FiniteDifferences.ts
@@ -146,7 +146,7 @@ export function derivative(
   xMin: number,
   xMax: number,
   binCount: number
-): Vector<number> {
+): Vector {
   const x = linspace(xMin, xMax, binCount);
   const y = x.map(f);
   const delta = x.getEntry(1) - x.getEntry(0);
diff --git a/src/applications/machine-learning/GradientDescent.ts b/src/applications/machine-learning/GradientDescent.ts
index e715d95..8f7ef81 100644
--- a/src/applications/machine-learning/GradientDescent.ts
+++ b/src/applications/machine-learning/GradientDescent.ts
@@ -20,7 +20,7 @@ export type GradientDescentParameters = {
 export function gradientDescent(parameters: Partial<GradientDescentParameters>): LearningAlgorithm {
   const { alpha = 0.1, maxIterations = 10000 } = parameters;
 
-  return function(initialTheta: Vector<number>, costFn: CostFunction) {
+  return function(initialTheta: Vector, costFn: CostFunction) {
     let theta = initialTheta;
     let i = 0;
     const zeros = theta.builder().zeros(theta.getDimension());
diff --git a/src/applications/machine-learning/LearningAlgorithm.ts b/src/applications/machine-learning/LearningAlgorithm.ts
index 223121a..90ba9ba 100644
--- a/src/applications/machine-learning/LearningAlgorithm.ts
+++ b/src/applications/machine-learning/LearningAlgorithm.ts
@@ -6,21 +6,18 @@ import { Vector } from '../../types/vector/Vector';
  */
 export interface Cost {
   cost: number;
-  gradient: Vector<number>;
+  gradient: Vector;
 }
 
 /**
  * A function that evaluates the cost of a set of parameters `theta`
  * @public
  */
-export type CostFunction = (theta: Vector<number>) => Cost;
+export type CostFunction = (theta: Vector) => Cost;
 
 /**
  * An function which, given an initial value of `theta` and a CostFunction,
  * will compute the optimal value of `theta`
  * @public
  */
-export type LearningAlgorithm = (
-  initialTheta: Vector<number>,
-  costFn: CostFunction
-) => Vector<number>;
+export type LearningAlgorithm = (initialTheta: Vector, costFn: CostFunction) => Vector;
diff --git a/src/applications/machine-learning/clustering/kMeansClustering.ts b/src/applications/machine-learning/clustering/kMeansClustering.ts
index d7d622f..d0ca511 100644
--- a/src/applications/machine-learning/clustering/kMeansClustering.ts
+++ b/src/applications/machine-learning/clustering/kMeansClustering.ts
@@ -10,14 +10,11 @@ interface KMeansParameters {
 }
 
 interface ClusteringResult {
-  centroids: Vector<number>[];
-  labels: Vector<number>;
+  centroids: Vector[];
+  labels: Vector;
 }
 
-export function kMeansClustering(
-  X: Matrix<number>,
-  params: Partial<KMeansParameters>
-): ClusteringResult {
+export function kMeansClustering(X: Matrix, params: Partial<KMeansParameters>): ClusteringResult {
   const rows = X.getRowVectors();
   const [m, n] = X.getShape();
   const { k = 5, norm = euclideanNorm, maxIterations = 100 } = params;
@@ -40,11 +37,7 @@ export function kMeansClustering(
   return { centroids, labels };
 }
 
-function updateLabels(
-  rows: Vector<number>[],
-  centroids: Vector<number>[],
-  norm: Norm
-): Vector<number> {
+function updateLabels(rows: Vector[], centroids: Vector[], norm: Norm): Vector {
   const m = rows.length;
   const vectorBuilder = rows[0].builder();
   return vectorBuilder.fromIndexFunction(m, i =>
@@ -52,11 +45,7 @@ function updateLabels(
   );
 }
 
-function getIndexOfClosestCentroid(
-  row: Vector<number>,
-  centroids: Vector<number>[],
-  norm: Norm
-): number {
+function getIndexOfClosestCentroid(row: Vector, centroids: Vector[], norm: Norm): number {
   let min = Number.MAX_VALUE;
   let index = -1;
 
@@ -71,10 +60,10 @@ function getIndexOfClosestCentroid(
   return index;
 }
 
-function updateCentroids(rows: Vector<number>[], labels: Vector<number>): Vector<number>[] {
+function updateCentroids(rows: Vector[], labels: Vector): Vector[] {
   const n = rows[0].getDimension();
   const zero = rows[0].builder().zeros(n);
-  const clusters: Vector<number>[][] = [];
+  const clusters: Vector[][] = [];
 
   rows.forEach((row, i) => {
     const label = labels.getEntry(i);
@@ -94,14 +83,14 @@ function updateCentroids(rows: Vector<number>[], labels: Vector<number>): Vector
   return centroids;
 }
 
-function initializeCentroids(X: Matrix<number>, k: number, n: number): Vector<number>[] {
+function initializeCentroids(X: Matrix, k: number, n: number): Vector[] {
   const vectorBuilder = X.vectorBuilder();
   const ops = X.ops();
   ops.randomNormal;
   const means = mean(X);
   const stdDevs = standardDeviation(X).map(x => 2 * x);
 
-  const cols: Vector<number>[] = [];
+  const cols: Vector[] = [];
   for (let i = 0; i < n; i++) {
     cols.push(vectorBuilder.randomNormal(k, means.getEntry(i), stdDevs.getEntry(i)));
   }
diff --git a/src/applications/machine-learning/kernels/GaussianKernel.ts b/src/applications/machine-learning/kernels/GaussianKernel.ts
index e7d9b52..6a77140 100644
--- a/src/applications/machine-learning/kernels/GaussianKernel.ts
+++ b/src/applications/machine-learning/kernels/GaussianKernel.ts
@@ -20,7 +20,7 @@ export function GaussianKernel(sigmaSquared: number): Kernel {
 /**
  * {@see SimilarityMetric}
  */
-function gaussianSimilarity(sigmaSquared: number, v1: Vector<number>, v2: Vector<number>): number {
+function gaussianSimilarity(sigmaSquared: number, v1: Vector, v2: Vector): number {
   const diff = euclideanNorm(v1.add(v2.scalarMultiply(-1)));
   return gaussian(diff, sigmaSquared);
 }
diff --git a/src/applications/machine-learning/kernels/Kernel.ts b/src/applications/machine-learning/kernels/Kernel.ts
index 27174aa..4bf4b68 100644
--- a/src/applications/machine-learning/kernels/Kernel.ts
+++ b/src/applications/machine-learning/kernels/Kernel.ts
@@ -9,4 +9,4 @@ import { Matrix } from '../../../types/matrix/Matrix';
  *
  * @public
  */
-export type Kernel = (data: Matrix<number>, trainingData?: Matrix<number>) => Matrix<number>;
+export type Kernel = (data: Matrix, trainingData?: Matrix) => Matrix;
diff --git a/src/applications/machine-learning/kernels/LinearKernel.ts b/src/applications/machine-learning/kernels/LinearKernel.ts
index 3cdb57f..8a81eee 100644
--- a/src/applications/machine-learning/kernels/LinearKernel.ts
+++ b/src/applications/machine-learning/kernels/LinearKernel.ts
@@ -10,7 +10,7 @@ import { Matrix } from '../../../types/matrix/Matrix';
  *
  * @public
  */
-export function LinearKernel(data: Matrix<number>): Matrix<number> {
+export function LinearKernel(data: Matrix): Matrix {
   const m = data.getNumberOfRows();
   const ones = data.builder().ones([m, 1]);
   return data.builder().augment(ones, data);
diff --git a/src/applications/machine-learning/kernels/RadialBasisFunction.ts b/src/applications/machine-learning/kernels/RadialBasisFunction.ts
index bebdb10..a23831a 100644
--- a/src/applications/machine-learning/kernels/RadialBasisFunction.ts
+++ b/src/applications/machine-learning/kernels/RadialBasisFunction.ts
@@ -8,7 +8,7 @@ import { Kernel } from './Kernel';
  *
  * @public
  */
-export type SimilarityMetric = (v1: Vector<number>, v2: Vector<number>) => number;
+export type SimilarityMetric = (v1: Vector, v2: Vector) => number;
 
 /**
  * Creates a {@link Kernel} for use in a {@link SupportVectorMachineClassifier}.
@@ -22,7 +22,7 @@ export type SimilarityMetric = (v1: Vector<number>, v2: Vector<number>) => numbe
  * @public
  */
 export function RadialBasisFunction(distanceMetric: SimilarityMetric): Kernel {
-  return (data: Matrix<number>, trainingData: Matrix<number> = data) => {
+  return (data: Matrix, trainingData: Matrix = data) => {
     const [m] = data.getShape();
     const [n] = trainingData.getShape();
 
diff --git a/src/applications/machine-learning/kernels/__tests__/RadialBasisFunction.spec.ts b/src/applications/machine-learning/kernels/__tests__/RadialBasisFunction.spec.ts
index f169489..4ef3200 100644
--- a/src/applications/machine-learning/kernels/__tests__/RadialBasisFunction.spec.ts
+++ b/src/applications/machine-learning/kernels/__tests__/RadialBasisFunction.spec.ts
@@ -8,8 +8,7 @@ describe('RadialBasisFunction', () => {
   it('constructs a similarity matrix given a similarity metric', () => {
     // Note: this is a distance metric rather than a similarity metric,
     // but for testing purposes it works just fine.
-    const metric = (v1: Vector<number>, v2: Vector<number>) =>
-      euclideanNorm(v1.add(v2.scalarMultiply(-1)));
+    const metric = (v1: Vector, v2: Vector) => euclideanNorm(v1.add(v2.scalarMultiply(-1)));
 
     const data = loadTestData('iris');
     const kerneled = RadialBasisFunction(metric)(data);
diff --git a/src/applications/machine-learning/models/Classifier.ts b/src/applications/machine-learning/models/Classifier.ts
index 301f624..887f0e8 100644
--- a/src/applications/machine-learning/models/Classifier.ts
+++ b/src/applications/machine-learning/models/Classifier.ts
@@ -13,7 +13,7 @@ export interface Classifier<H> {
    * @param target - A {@link Vector} whose entries are the target values in the training set
    * @public
    */
-  train(data: Matrix<number>, target: Vector<number>): void;
+  train(data: Matrix, target: Vector): void;
 
   /**
    * Uses the learned parameters to make predictions for the probability of an event
@@ -25,7 +25,7 @@ export interface Classifier<H> {
    * @param data - A {@link Vector} whose rows are the observations in the test set
    * @public
    */
-  predictProbabilities(data: Matrix<number>): Vector<number>;
+  predictProbabilities(data: Matrix): Vector;
 
   /**
    * Uses the learned parameters to make predictions based on a set of input data.
@@ -37,7 +37,7 @@ export interface Classifier<H> {
    * @param pThreshold - The probability threshold above which an event will be predicted
    * @public
    */
-  predict(data: Matrix<number>, pThreshold: number): Vector<number>;
+  predict(data: Matrix, pThreshold: number): Vector;
 
   /**
    * Return the full set of hyperparameters used to train the model, including defaults.
diff --git a/src/applications/machine-learning/models/LinearRegressor.ts b/src/applications/machine-learning/models/LinearRegressor.ts
index aeececa..eea1bae 100644
--- a/src/applications/machine-learning/models/LinearRegressor.ts
+++ b/src/applications/machine-learning/models/LinearRegressor.ts
@@ -28,7 +28,7 @@ export type LinearRegressorHyperparams = GradientDescentParameters & {
  */
 export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
   private readonly _hyperParameters: Readonly<Partial<LinearRegressorHyperparams>>;
-  private _theta: Vector<number> | undefined;
+  private _theta: Vector | undefined;
 
   constructor(hyperParameters: Partial<LinearRegressorHyperparams>) {
     this._hyperParameters = Object.freeze(hyperParameters);
@@ -40,7 +40,7 @@ export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
    * `undefined` if the model has not been trained.
    * @public
    */
-  public getParameters(): Vector<number> | undefined {
+  public getParameters(): Vector | undefined {
     return this._theta;
   }
 
@@ -57,7 +57,7 @@ export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
   /**
    * {@inheritDoc Regressor.train}
    */
-  public train(data: Matrix<number>, target: Vector<number>): void {
+  public train(data: Matrix, target: Vector): void {
     const initialTheta = FloatVector.builder().random(data.getNumberOfColumns() + 1, -0.01, 0.01);
     this._theta = gradientDescent(this._hyperParameters)(initialTheta, theta => ({
       cost: this.calculateCost(data, target, theta),
@@ -68,16 +68,12 @@ export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
   /**
    * {@inheritDoc Regressor.predict}
    */
-  public predict(data: Matrix<number>): Vector<number> {
+  public predict(data: Matrix): Vector {
     if (!this._theta) throw new Error(`Cannot call predict before train`);
     return this.makePredictions(data, this._theta);
   }
 
-  private calculateCost(
-    data: Matrix<number>,
-    target: Vector<number>,
-    theta: Vector<number>
-  ): number {
+  private calculateCost(data: Matrix, target: Vector, theta: Vector): number {
     const { lambda } = this.getHyperParameters();
     const predictions = this.makePredictions(data, theta);
     const residuals = target.scalarMultiply(-1).add(predictions);
@@ -92,11 +88,7 @@ export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
     return meanSquaredError + regularizationTerm;
   }
 
-  private calculateGradient(
-    data: Matrix<number>,
-    target: Vector<number>,
-    theta: Vector<number>
-  ): Vector<number> {
+  private calculateGradient(data: Matrix, target: Vector, theta: Vector): Vector {
     const { lambda } = this.getHyperParameters();
     const m = data.getNumberOfRows();
 
@@ -113,7 +105,7 @@ export class LinearRegressor implements Regressor<LinearRegressorHyperparams> {
     return gradientTerm.add(regularizationTerm);
   }
 
-  private makePredictions(data: Matrix<number>, theta: Vector<number>): Vector<number> {
+  private makePredictions(data: Matrix, theta: Vector): Vector {
     return LinearKernel(data).apply(theta);
   }
 
diff --git a/src/applications/machine-learning/models/LogisticRegressionClassifier.ts b/src/applications/machine-learning/models/LogisticRegressionClassifier.ts
index 149cb29..1053069 100644
--- a/src/applications/machine-learning/models/LogisticRegressionClassifier.ts
+++ b/src/applications/machine-learning/models/LogisticRegressionClassifier.ts
@@ -28,7 +28,7 @@ export type LogisticRegressionHyperparams = GradientDescentParameters & {
  */
 export class LogisticRegressionClassifier implements Classifier<LogisticRegressionHyperparams> {
   private readonly _hyperParameters: Readonly<Partial<LogisticRegressionHyperparams>>;
-  private _theta: Vector<number> | undefined;
+  private _theta: Vector | undefined;
 
   constructor(hyperParameters: Partial<LogisticRegressionHyperparams>) {
     this._hyperParameters = Object.freeze(hyperParameters);
@@ -39,7 +39,7 @@ export class LogisticRegressionClassifier implements Classifier<LogisticRegressi
    * `undefined` if the model has not been trained.
    * @public
    */
-  public getParameters(): Vector<number> | undefined {
+  public getParameters(): Vector | undefined {
     return this._theta;
   }
 
@@ -56,7 +56,7 @@ export class LogisticRegressionClassifier implements Classifier<LogisticRegressi
   /**
    * {@inheritDoc Classifier.train}
    */
-  public train(data: Matrix<number>, target: Vector<number>): void {
+  public train(data: Matrix, target: Vector): void {
     const initialTheta = FloatVector.builder().random(data.getNumberOfColumns() + 1, -0.01, 0.01);
     this._theta = gradientDescent(this._hyperParameters)(initialTheta, theta => ({
       cost: this.calculateCost(data, target, theta),
@@ -67,7 +67,7 @@ export class LogisticRegressionClassifier implements Classifier<LogisticRegressi
   /**
    * {@inheritDoc Classifier.predictProbabilities}
    */
-  public predictProbabilities(data: Matrix<number>): Vector<number> {
+  public predictProbabilities(data: Matrix): Vector {
     if (!this._theta) throw new Error(`Cannot call predictProbabilities before train`);
     return this.makeProbabilityPredictions(data, this._theta);
   }
@@ -75,30 +75,22 @@ export class LogisticRegressionClassifier implements Classifier<LogisticRegressi
   /**
    * {@inheritDoc Classifier.predict}
    */
-  public predict(data: Matrix<number>): Vector<number> {
+  public predict(data: Matrix): Vector {
     if (!this._theta) throw new Error(`Cannot call predict before train`);
     return this.makePredictions(data, this._theta);
   }
 
-  private makePredictions(
-    data: Matrix<number>,
-    theta: Vector<number>,
-    threshold?: number
-  ): Vector<number> {
+  private makePredictions(data: Matrix, theta: Vector, threshold?: number): Vector {
     return this.makeProbabilityPredictions(data, theta).map(p => (p > (threshold || 0.5) ? 1 : 0));
   }
 
-  private makeProbabilityPredictions(data: Matrix<number>, theta: Vector<number>): Vector<number> {
+  private makeProbabilityPredictions(data: Matrix, theta: Vector): Vector {
     return LinearKernel(data)
       .apply(theta)
       .map(sigmoid);
   }
 
-  private calculateCost(
-    data: Matrix<number>,
-    target: Vector<number>,
-    theta: Vector<number>
-  ): number {
+  private calculateCost(data: Matrix, target: Vector, theta: Vector): number {
     const { lambda } = this.getHyperParameters();
     const probabilities = this.makeProbabilityPredictions(data, theta);
 
@@ -126,11 +118,7 @@ export class LogisticRegressionClassifier implements Classifier<LogisticRegressi
   /**
    * {@inheritDoc GradientDescentClassifier.calculateGradient}
    */
-  private calculateGradient(
-    data: Matrix<number>,
-    target: Vector<number>,
-    theta: Vector<number>
-  ): Vector<number> {
+  private calculateGradient(data: Matrix, target: Vector, theta: Vector): Vector {
     const m = data.getNumberOfRows();
     const { lambda } = this.getHyperParameters();
 
diff --git a/src/applications/machine-learning/models/Regressor.ts b/src/applications/machine-learning/models/Regressor.ts
index 2c277d4..61674e5 100644
--- a/src/applications/machine-learning/models/Regressor.ts
+++ b/src/applications/machine-learning/models/Regressor.ts
@@ -13,7 +13,7 @@ export interface Regressor<H> {
    * @param target - A {@link Vector} whose entries are the target values in the training set
    * @public
    */
-  train(data: Matrix<number>, target: Vector<number>): void;
+  train(data: Matrix, target: Vector): void;
 
   /**
    * Uses the learned parameters to make predictions based on a set of input data.
@@ -24,7 +24,7 @@ export interface Regressor<H> {
    * @param data - A {@link Vector} whose rows are the observations in the test set
    * @public
    */
-  predict(data: Matrix<number>): Vector<number>;
+  predict(data: Matrix): Vector;
 
   /**
    * Return the full set of hyperparameters used to train the model, including defaults.
diff --git a/src/applications/machine-learning/models/SupportVectorMachineClassifier.ts b/src/applications/machine-learning/models/SupportVectorMachineClassifier.ts
index 1f936a4..705f333 100644
--- a/src/applications/machine-learning/models/SupportVectorMachineClassifier.ts
+++ b/src/applications/machine-learning/models/SupportVectorMachineClassifier.ts
@@ -34,8 +34,8 @@ export type SupportVectorMachineHyperparams = GradientDescentParameters & {
  */
 export class SupportVectorMachineClassifier implements Classifier<SupportVectorMachineHyperparams> {
   private readonly _hyperParameters: Readonly<Partial<SupportVectorMachineHyperparams>>;
-  private _weights: Vector<number> | undefined;
-  private _trainingData: Matrix<number> | undefined;
+  private _weights: Vector | undefined;
+  private _trainingData: Matrix | undefined;
 
   constructor(hyperParameters: Partial<SupportVectorMachineHyperparams>) {
     this._hyperParameters = Object.freeze(hyperParameters);
@@ -46,14 +46,14 @@ export class SupportVectorMachineClassifier implements Classifier<SupportVectorM
    * `undefined` if the model has not been trained.
    * @public
    */
-  public getParameters(): Vector<number> | undefined {
+  public getParameters(): Vector | undefined {
     return this._weights;
   }
 
   /**
    * {@inheritDoc Classifier.train}
    */
-  public train(data: Matrix<number>, target: Vector<number>): void {
+  public train(data: Matrix, target: Vector): void {
     const { kernel } = this.getHyperParameters();
 
     this._trainingData = data;
@@ -69,14 +69,14 @@ export class SupportVectorMachineClassifier implements Classifier<SupportVectorM
   /**
    * {@inheritDoc Classifier.predictProbabilities}
    */
-  public predictProbabilities(_data: Matrix<number>): Vector<number> {
+  public predictProbabilities(_data: Matrix): Vector {
     throw Error(`Probability predictions not implemented for SVM Classifier`);
   }
 
   /**
    * {@inheritDoc Classifier.predict}
    */
-  public predict(data: Matrix<number>): Vector<number> {
+  public predict(data: Matrix): Vector {
     if (!this._weights) throw new Error(`Cannot call predict before train`);
 
     const { kernel } = this.getHyperParameters();
@@ -95,16 +95,12 @@ export class SupportVectorMachineClassifier implements Classifier<SupportVectorM
     };
   }
 
-  private makePredictions(X: Matrix<number>, weights: Vector<number>): Vector<number> {
+  private makePredictions(X: Matrix, weights: Vector): Vector {
     const scores = this.calculateScores(X, weights);
     return scores.map(score => (score > 0 ? 1 : 0));
   }
 
-  private calculateCost(
-    X: Matrix<number>,
-    target: Vector<number>,
-    weights: Vector<number>
-  ): number {
+  private calculateCost(X: Matrix, target: Vector, weights: Vector): number {
     const [m] = X.getShape();
     const scores = this.calculateScores(X, weights);
     const costs = scores.map((score, i) => {
@@ -114,11 +110,7 @@ export class SupportVectorMachineClassifier implements Classifier<SupportVectorM
     return totalCost / m;
   }
 
-  private calculateGradient(
-    X: Matrix<number>,
-    target: Vector<number>,
-    weights: Vector<number>
-  ): Vector<number> {
+  private calculateGradient(X: Matrix, target: Vector, weights: Vector): Vector {
     const [m] = X.getShape();
 
     const scores = this.calculateScores(X, weights);
@@ -144,7 +136,7 @@ export class SupportVectorMachineClassifier implements Classifier<SupportVectorM
     return unscaledGradient.scalarMultiply(1 / m);
   }
 
-  private calculateScores(X: Matrix<number>, weights: Vector<number>): Vector<number> {
+  private calculateScores(X: Matrix, weights: Vector): Vector {
     return X.apply(weights);
   }
 
diff --git a/src/applications/statistics/PrincipalComponentAnalysis.ts b/src/applications/statistics/PrincipalComponentAnalysis.ts
index 5629117..a9d16a8 100644
--- a/src/applications/statistics/PrincipalComponentAnalysis.ts
+++ b/src/applications/statistics/PrincipalComponentAnalysis.ts
@@ -105,10 +105,7 @@ export type DimensionReductionOptions = DimensionReductionType & {
  *          be explained by the remaining columns
  * @public
  */
-export function reduceDimensions(
-  A: Matrix<number>,
-  options: DimensionReductionOptions
-): Matrix<number> {
+export function reduceDimensions(A: Matrix, options: DimensionReductionOptions): Matrix {
   let keep = A.getNumberOfColumns();
   if (hasKeep(options) && (options.keep > keep || options.keep < 0)) {
     throw Error(`Cannot keep ${options.keep} dimensions of a matrix with ${keep} columns`);
diff --git a/src/applications/statistics/__tests__/LeastSquares.spec.ts b/src/applications/statistics/__tests__/LeastSquares.spec.ts
index 12a5ef5..57c3ce6 100644
--- a/src/applications/statistics/__tests__/LeastSquares.spec.ts
+++ b/src/applications/statistics/__tests__/LeastSquares.spec.ts
@@ -82,7 +82,7 @@ describe('LeastSquares', () => {
   describe('calculateGeneralLeastSquaresApproximation', () => {
     test('calculates a quadratic regression', () => {
       const data = singleVariableTestData.getRowVectors();
-      const quadraticTemplate = (coefficients: Vector<number>) => (inputs: Vector<number>) => {
+      const quadraticTemplate = (coefficients: Vector) => (inputs: Vector) => {
         const x = inputs.getEntry(0);
         const constantTerm = coefficients.getEntry(0);
         const linearTerm = coefficients.getEntry(1) * x;
diff --git a/src/operations/__tests__/GaussJordan.spec.ts b/src/operations/__tests__/GaussJordan.spec.ts
index b018f26..6a02481 100644
--- a/src/operations/__tests__/GaussJordan.spec.ts
+++ b/src/operations/__tests__/GaussJordan.spec.ts
@@ -227,7 +227,7 @@ describe('GaussJordan', () => {
       const I = eye(3);
       const iInv = inverse(I);
       expect(iInv).not.toBeUndefined;
-      expect((iInv as Matrix<number>).equals(I)).toBe(true);
+      expect((iInv as Matrix).equals(I)).toBe(true);
     });
 
     test('calculates the inverse of a square matrix', () => {
@@ -239,7 +239,7 @@ describe('GaussJordan', () => {
         [0.6, -0.7],
         [-0.2, 0.4]
       ]);
-      const aInv = inverse(A) as Matrix<number>;
+      const aInv = inverse(A) as Matrix;
       expect(aInv).not.toBeUndefined;
       expect(aInv.equals(expectedInverse)).toBe(true);
     });
diff --git a/src/types/matrix/ArrayMatrix.ts b/src/types/matrix/ArrayMatrix.ts
index 7731df7..e693e4c 100644
--- a/src/types/matrix/ArrayMatrix.ts
+++ b/src/types/matrix/ArrayMatrix.ts
@@ -18,7 +18,7 @@ import { MatrixBuilder } from './MatrixBuilder';
  *
  * @public
  */
-export abstract class ArrayMatrix<S> implements Matrix<S> {
+export abstract class ArrayMatrix<S = number> implements Matrix<S> {
   private readonly _data: MatrixData<S>;
 
   /**
diff --git a/src/types/matrix/Matrix.ts b/src/types/matrix/Matrix.ts
index aed63f8..83fce1e 100644
--- a/src/types/matrix/Matrix.ts
+++ b/src/types/matrix/Matrix.ts
@@ -32,7 +32,7 @@ export interface MatrixConstructor<S, V extends Vector<S>, M extends Matrix<S>>
  * A generalized Matrix - one of the core data types
  * @public
  */
-export interface Matrix<S> extends LinearTransformation<Vector<S>, Vector<S>> {
+export interface Matrix<S = number> extends LinearTransformation<Vector<S>, Vector<S>> {
   /**
    * Returns a {@link ScalarOperations} object which will allow consumers to work generically
    * with the scalars contained in the vector.
diff --git a/src/types/matrix/SparseMatrix.ts b/src/types/matrix/SparseMatrix.ts
index d663416..e3c13da 100644
--- a/src/types/matrix/SparseMatrix.ts
+++ b/src/types/matrix/SparseMatrix.ts
@@ -31,7 +31,7 @@ type MutableSparseMatrixData<S> = Map<number, Map<number, S>>;
  *
  * @public
  */
-export abstract class SparseMatrix<S> implements Matrix<S> {
+export abstract class SparseMatrix<S = number> implements Matrix<S> {
   private readonly _shape: MatrixShape;
   private readonly _sparseData: SparseMatrixData<S>;
 
diff --git a/src/types/vector/ArrayVector.ts b/src/types/vector/ArrayVector.ts
index 01f32fd..1a62df6 100644
--- a/src/types/vector/ArrayVector.ts
+++ b/src/types/vector/ArrayVector.ts
@@ -13,7 +13,7 @@ import { Matrix } from '../matrix/Matrix';
  *
  * @public
  */
-export abstract class ArrayVector<S> implements Vector<S> {
+export abstract class ArrayVector<S = number> implements Vector<S> {
   private readonly _data: VectorData<S>;
 
   /**
diff --git a/src/types/vector/SparseVector.ts b/src/types/vector/SparseVector.ts
index 1691d18..9b10a8e 100644
--- a/src/types/vector/SparseVector.ts
+++ b/src/types/vector/SparseVector.ts
@@ -32,7 +32,7 @@ export function isSparse<S>(vector: Vector<S>): vector is SparseVector<S> {
  *
  * @public
  */
-export abstract class SparseVector<S> implements Vector<S> {
+export abstract class SparseVector<S = number> implements Vector<S> {
   private readonly _dimension: number;
   private readonly _sparseData: SparseVectorData<S>;
 
diff --git a/src/types/vector/Vector.ts b/src/types/vector/Vector.ts
index 75032c4..600f8f3 100644
--- a/src/types/vector/Vector.ts
+++ b/src/types/vector/Vector.ts
@@ -22,7 +22,7 @@ export interface VectorConstructor<S, V extends Vector<S>> {
  * A generalized Vector - one of the core data types
  * @public
  */
-export interface Vector<S> {
+export interface Vector<S = number> {
   /**
    * Returns a {@link ScalarOperations} object which will allow consumers to work generically
    * with the scalars contained in the vector.
diff --git a/src/utilities/aliases.ts b/src/utilities/aliases.ts
index fddabba..e14eb76 100644
--- a/src/utilities/aliases.ts
+++ b/src/utilities/aliases.ts
@@ -10,7 +10,7 @@ const mb = FloatMatrix.builder();
  * Creates a new {@link Vector} of numbers.  See {@link VectorBuilder.fromArray}
  * @public
  */
-export function vec(data: number[]): Vector<number> {
+export function vec(data: number[]): Vector {
   return vb.fromArray(data);
 }
 
@@ -18,7 +18,7 @@ export function vec(data: number[]): Vector<number> {
  * Creates a new {@link Matrix} of numbers.  See {@link MatrixBuilder.fromArray}
  * @public
  */
-export function mat(data: number[][]): Matrix<number> {
+export function mat(data: number[][]): Matrix {
   return mb.fromArray(data);
 }
 
@@ -26,7 +26,7 @@ export function mat(data: number[][]): Matrix<number> {
  * Creates a new identity matrix of size `size`.  See {@link MatrixBuilder.identity}
  * @public
  */
-export function eye(size: number): Matrix<number> {
+export function eye(size: number): Matrix {
   return mb.identity(size);
 }
 
@@ -34,13 +34,13 @@ export function eye(size: number): Matrix<number> {
  * Creates a new vector of all 1s.  See {@link VectorBuilder.ones}
  * @public
  */
-export function ones(entries: number): Vector<number>;
+export function ones(entries: number): Vector;
 /**
  * Creates a new matrix of all 1s.  See {@link MatrixBuilder.ones}
  * @public
  */
-export function ones(shape: MatrixShape): Matrix<number>;
-export function ones(shapeOrDim: number | MatrixShape): Matrix<number> | Vector<number> {
+export function ones(shape: MatrixShape): Matrix;
+export function ones(shapeOrDim: number | MatrixShape): Matrix | Vector {
   return Array.isArray(shapeOrDim) ? mb.ones(shapeOrDim) : vb.ones(shapeOrDim);
 }
 
@@ -48,13 +48,13 @@ export function ones(shapeOrDim: number | MatrixShape): Matrix<number> | Vector<
  * Creates a new vector of all 0s.  See {@link VectorBuilder.zeros}
  * @public
  */
-export function zeros(entries: number): Vector<number>;
+export function zeros(entries: number): Vector;
 /**
  * Creates a new matrix of all 0s.  See {@link MatrixBuilder.zeros}
  * @public
  */
-export function zeros(shape: MatrixShape): Matrix<number>;
-export function zeros(shapeOrDim: number | MatrixShape): Matrix<number> | Vector<number> {
+export function zeros(shape: MatrixShape): Matrix;
+export function zeros(shapeOrDim: number | MatrixShape): Matrix | Vector {
   return Array.isArray(shapeOrDim) ? mb.zeros(shapeOrDim) : vb.zeros(shapeOrDim);
 }
 
@@ -62,6 +62,6 @@ export function zeros(shapeOrDim: number | MatrixShape): Matrix<number> | Vector
  * Creates a new matrix with the specified entries on the diagonal.  See {@link MatrixBuilder.diagonal}
  * @public
  */
-export function diag(elements: number[]): Matrix<number> {
+export function diag(elements: number[]): Matrix {
   return mb.diagonal(vec(elements));
 }
diff --git a/test/utils/rounding.ts b/test/utils/rounding.ts
index 1d4afde..bb47ffb 100644
--- a/test/utils/rounding.ts
+++ b/test/utils/rounding.ts
@@ -1,11 +1,11 @@
 import { Vector } from '../../src/types/vector/Vector';
 import { Matrix } from '../../src/types/matrix/Matrix';
 
-export function roundMatrix(M: Matrix<number>, decimals: number): Matrix<number> {
+export function roundMatrix(M: Matrix, decimals: number): Matrix {
   return M.map(x => round(x, decimals));
 }
 
-export function roundVector(v: Vector<number>, decimals: number): Vector<number> {
+export function roundVector(v: Vector, decimals: number): Vector {
   return v.map(x => round(x, decimals));
 }
 
diff --git a/test/utils/testData.ts b/test/utils/testData.ts
index 6887741..5c17e90 100644
--- a/test/utils/testData.ts
+++ b/test/utils/testData.ts
@@ -5,17 +5,17 @@ import csvWriter from 'csv-stringify/lib/sync';
 import { Matrix } from '../../src/types/matrix/Matrix';
 import { mat } from '../../src/utilities/aliases';
 
-const cache: Map<string, Matrix<number>> = new Map();
+const cache: Map<string, Matrix> = new Map();
 
-export function loadTestData(name: string): Matrix<number> {
-  if (cache.has(name)) return cache.get(name) as Matrix<number>;
+export function loadTestData(name: string): Matrix {
+  if (cache.has(name)) return cache.get(name) as Matrix;
 
   const contents = fs.readFileSync(path.resolve(__dirname, '../data/', name + '.csv')).toString();
   const data = (csvParser(contents) as any[][]).map(row => row.map(entry => parseFloat(entry)));
   return mat(data);
 }
 
-export function writeTestData(name: string, matrix: Matrix<number>): void {
+export function writeTestData(name: string, matrix: Matrix): void {
   const contents = csvWriter(matrix.toArray());
   fs.writeFileSync(path.resolve(__dirname, '../data/', name + '.csv'), contents);
 }