data library

All data packages in a single import.

Classes

BernoulliDistribution
The Bernoulli distribution is a discrete probability distribution which takes value 1 with probability p and value 0 with probability q = 1 − p.
BigIntDataType
BigIntEquality
BigIntField
BinomialDistribution
The Binomial distribution is a discrete probability distribution which describes the number of successes in a series of independent yes/no experiments all with the same probability of success.
BooleanDataType
Complex
A complex number of the form a + b*i.
ComplexDataType
ComplexEquality
ComplexField
ContinuousDistribution
Abstract interface of all continuous distributions.
CurveFit
Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints.
CurveFitResult
Generic result of a curve fitting.
DataType<T>
Descriptor of a data type T, how it is efficiently represented and stored in memory, and strategy of how common operations work.
DegenerateDistribution
The Degenerate distribution, a continuous probability distribution that is certain to take the value k.
DiscreteDistribution
Abstract interface of all continuous distributions.
Distribution<T>
Abstract interface of all distributions.
Equality<T>
Encapsulates equality between and the hash code of objects.
ExponentialDistribution
The exponential distribution.
Field<T>
Encapsulates a mathematical field.
Float32DataType
Float64DataType
FloatDataType<L extends List<double>>
FloatEquality
FloatField
Fraction
A rational number.
FractionDataType
FractionEquality
FractionField
GammaDistribution
The gamma distribution.
Int16DataType
Int32DataType
Int64DataType
Int8DataType
IntegerDataType<L extends List<int>>
IntegerEquality
IntegerField
InverseGammaDistribution
The inverse gamma distribution.
Jackknife<T>
A deterministic resampling technique to estimate variance, bias, and confidence intervals.
Layout
Immutable object describing a multi-dimensional data layout in a flat list of values.
LevenbergMarquardt
The Levenberg–Marquardt algorithm, also known as the damped least-squares method, is used to solve non-linear least squares problems.
LevenbergMarquardtResult
LogNormalDistribution
Log-normal distribution of a random variable whose logarithm is normally distributed.
Matrix<T>
Abstract matrix type.
ModuloDataType<T>
ModuloEquality<T>
ModuloField<T>
NaturalEquality<T>
The natural and canonical equality of objects.
NegativeBinomialDistribution
The Binomial distribution is a discrete probability distribution which models the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified (non-random) number of failures (denoted r) occurs.
NormalDistribution
Normal (or Gaussian) distribution described by the mean or expectation of the distribution and its standard deviation.
NullableDataType<T>
Some DataType instances do not support null values in the way they represent their data. This wrapper turns those types into nullable ones.
NullableList<T>
A list with null values, where the null values are tracked in a separate BitList. For certain types of typed lists, this is the only way to track null values.
NumericDataType
NumericEquality
NumericField
ObjectDataType<T>
ParametrizedUnaryFunction<T>
Abstract factory of parametrized unary functions of type UnaryFunction<T>.
PoissonDistribution
The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.
Polynomial<T>
Abstract polynomial type.
PolynomialDivision<T>
Data holder for the result of a polynomial division.
PolynomialRegression
Polynomial least-squares regression, in which the relationship between the independent elements xs and the dependent elements ys is modelled as a polynomial of a given degree.
PolynomialRegressionResult
Quaternion
A quaternion number of the form w + x*i + y*j + z*k.
QuaternionDataType
QuaternionEquality
QuaternionField
RademacherDistribution
The Rademacher distribution is a discrete probability function which takes value 1 with probability 1/2 and value −1 with probability 1/2.
StringDataType
StringEquality
StudentDistribution
The Student's t-distribution.
Tensor<T>
A multi-dimensional fixed-size container of items of a specific type.
TensorPrinter<T>
Uint16DataType
Uint32DataType
Uint64DataType
Uint8DataType
UniformDiscreteDistribution
A discrete uniform distribution between a and b, for details see https://en.wikipedia.org/wiki/Discrete_uniform_distribution.
UniformDistribution
The continuous uniform distribution between the bounds a and b. The distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds.
Vector<T>
Abstract vector type.
WeibullDistribution
The Weibull distribution.

Enums

ConvolutionMode
Convolution mode, i.e. how the borders are handled.
IntegrateWarning
Integration warnings that can be triggered for badly behaving functions or ill defined parameters.
MatrixFormat
Formats of matrices.
PolynomialFormat
Formats of polynomials.
VectorFormat
Formats of vectors.

Mixins

CompareOperators<T>
A generic mixin that provides standard comparison operators like <, <=, >= and > provided the class is Comparable.

Extensions

AddMatrixExtension on Matrix<T>
AddPolynomialExtension on Polynomial<T>
AddVectorExtension on Vector<T>
ApplyMatrixExtension on Matrix<T>
BinaryOperationMatrixExtension on Matrix<T>
BinaryOperationVectorExtension on Vector<T>
BroadcastLayoutExtension on Layout
CastMatrixExtension on Matrix<T>
CastVectorExtension on Vector<T>
CholeskyDecompositionExtension on Matrix<T>
CollapseLayoutExtension on Layout
CollapseTensorExtension on Tensor<T>
ColumnMatrixExtension on Vector<T>
ColumnVectorExtension on Matrix<T>
CompareMatrixExtension on Matrix<T>
ComparePolynomialExtension on Polynomial<T>
CompareVectorExtension on Vector<T>
ComparisonTensorExtension on Tensor<T>
CompoundComparator on Comparator<T>
CompoundIterableComparator on Iterable<Comparator<T>>
ContiguousTensorExtension on Tensor<T>
ConvolutionMatrixExtension on Matrix<T>
ConvolutionVectorExtension on Vector<T>
CopyTensorExtension on Tensor<T>
DiagonalMatrixExtension on Vector<T>
DiagonalVectorExtension on Matrix<T>
DifferentiatePolynomialExtension on Polynomial<T>
DivPolynomialExtension on Polynomial<T>
DivVectorExtension on Vector<T>
DotVectorExtension on Vector<T>
EigenvalueDecompositionExtension on Matrix<T>
ElementLayoutExtension on Layout
ElementTensorExtension on Tensor<T>
ExpandLayoutExtension on Layout
ExpandTensorExtension on Tensor<T>
FlipLayoutExtension on Layout
FlippedHorizontalMatrixExtension on Matrix<T>
FlippedVerticalMatrixExtension on Matrix<T>
FlipTensorExtension on Tensor<T>
IndexMatrixExtension on Matrix<T>
IndexVectorExtension on Vector<T>
IntegratePolynomialExtension on Polynomial<T>
IterableIntExtension on Iterable<int>
IterableNumExtension on Iterable<num>
IterableVectorExtension on Iterable<T>
IteratorMatrixExtension on Matrix<T>
LargestComparator on Comparator<T>
LerpMatrixExtension on Matrix<T>
LerpPolynomialExtension on Polynomial<T>
LerpVectorExtension on Vector<T>
LexicographicalComparator on Comparator<T>
ListPolynomialExtension on List<T>
ListVectorExtension on List<T>
LogicalTensorExtension on Tensor<bool>
LUDecompositionExtension on Matrix<T>
MagnitudeVectorExtension on Vector<T>
MathTensorExtension on Tensor<T>
MatrixMatrixMultiplicationMatrixExtension on Matrix<T>
MatrixVectorMultiplicationVectorExtension on Matrix<T>
MinMaxComparator on Comparator<T>
MulMatrixExtension on Matrix<T>
MulPolynomialExtension on Polynomial<T>
MulVectorExtension on Vector<T>
NegMatrixExtension on Matrix<T>
NegPolynomialExtension on Polynomial<T>
NegVectorExtension on Vector<T>
NormDoubleExtension on Matrix<double>
NormIntegerExtension on Matrix<int>
NormNumberExtension on Matrix<num>
NullsFirstComparator on Comparator<T>
NullsLastComparator on Comparator<T>
OperationTensorExtension on Tensor<T>
OrderedComparator on Comparator<T>
OverlayMatrixExtension on Matrix<T>
OverlayVectorExtension on Vector<T>
PolynomialListExtension on Polynomial<T>
PredicateComparator on Comparator<T>
QRDecompositionExtension on Matrix<T>
RangeLayoutExtension on Layout
RangeMatrixExtension on Matrix<T>
RangeTensorExtension on Tensor<T>
RangeVectorExtension on Vector<T>
ReshapeTensorExtension on Tensor<T>
ResultOfComparator on Comparator<R>
ReversedComparator on Comparator<T>
ReversedVectorExtension on Vector<T>
RootsPolynomialExtension on Polynomial<T>
RotatedMatrixExtension on Matrix<T>
RowMatrixExtension on Vector<T>
RowVectorExtension on Matrix<T>
SearchComparator on Comparator<T>
ShiftPolynomialExtension on Polynomial<T>
SingularValueDecompositionExtension on Matrix<T>
SmallestComparator on Comparator<T>
SolverExtension on Matrix<T>
SortComparator on Comparator<T>
SubMatrixExtension on Matrix<T>
SubPolynomialExtension on Polynomial<T>
SubVectorExtension on Vector<T>
SumVectorExtension on Vector<T>
TestingMatrixExtension on Matrix<T>
ToObjectTensorExtension on Tensor<T>
ToTensorIterableExtension on Iterable<T>
TransformedMatrixExtension on Matrix<T>
TransformedVectorExtension on Vector<T>
TransposedMatrixExtension on Matrix<T>
TransposeLayoutExtension on Layout
TransposeTensorExtension on Tensor<T>
UnaryOperationMatrixExtension on Matrix<T>
UnaryOperationVectorExtension on Vector<T>
UnmodifiableMatrixExtension on Matrix<T>
UnmodifiablePolynomialExtension on Polynomial<T>
UnmodifiableVectorExtension on Vector<T>
VectorListExtension on Vector<T>

Functions

beta(num x, num y) double
Beta function based on the gamma function.
betacf_(num x, num a, num b) double
Evaluates the continued fraction for incomplete beta function by modified Lentz's method.
betaLn(num x, num y) double
Logarithm of the beta function based on the gammaLn function.
combination(num n, num k) double
Returns the combinations based on the gamma function.
combinationLn(num n, num k) double
Returns the logarithm of the combinations based on the gammaLn function.
derivative(UnaryFunction<double> function, double x, {int derivative = 1, int accuracy = 2, double epsilon = 1e-5}) double
Returns the numerical derivative of the provided function function at x.
editDistance(String a, String b) int
Computes the Levenshtein edit distance between two strings a and b: https://en.wikipedia.org/wiki/Levenshtein_distance
erf(num x) double
Returns an approximation of the error function, for details see https://en.wikipedia.org/wiki/Error_function.
erfc(num x) double
Returns the complementary error function.
erfcInv(num x) double
Returns the inverse complementary error function.
erfInv(num x) double
Returns the inverse error function.
explicitComparator<T>(Iterable<T> iterable) Comparator<T>
Returns an explicit Comparator based on an iterable of elements.
factorial(num n) double
Returns the factorial based on the gamma function.
factorialLn(num n) double
Returns the logarithm of the factorial based on the gammaLn function.
fft(List<Complex> values, {bool inverse = false}) List<Complex>
Performs an in-place Discrete Fast Fourier transformation on the provided values. If necessary, extends the size the provided list to a power of two. Returns the modified collection of transformed values.
gamma(num x) double
Returns an approximation of the gamma function, for details see https://en.wikipedia.org/wiki/Gamma_function.
gammaLn(num x) double
Returns the natural logarithm of the gamma function.
gammap(num a, num x) double
gammapInv(num p, num a) double
geometricSpaced(double start, double stop, {int count = 10, bool includeEndpoint = true, DataType<double>? dataType, VectorFormat? format}) Vector<double>
Generates a Vector with a sequence of count evenly spaced values on a log scale (a geometric progression) on the interval between start and stop.
ibeta(num x, num a, num b) double
Incomplete beta function.
ibetaInv(num p, num a, num b) double
Inverse of the incomplete beta function.
integrate(UnaryFunction<double> function, double a, double b, {int depth = 6, double epsilon = 1e-6, Iterable<double> poles = const [], void onWarning(IntegrateWarning type, double x)?}) double
Returns the numerical integration of the provided function from a to b, that is the result of int(f(x), dx=a..b).
lagrangeInterpolation<T>(DataType<T> dataType, {required Vector<T> xs, required Vector<T> ys}) UnaryFunction<T>
A function providing a Lagrange polynomial interpolation through the unique sample points xs and ys. Related to Polynomial.lagrange.
linearInterpolation<T>(DataType<T> dataType, {required Vector<T> xs, required Vector<T> ys, T? left, T? right}) UnaryFunction<T>
A function providing linear interpolation of a discrete monotonically increasing set of sample points xs and ys. Returns left or right, if the point is outside the data range, by default extrapolate linearly.
linearSpaced(double start, double stop, {int count = 10, bool includeEndpoint = true, DataType<double>? dataType, VectorFormat? format}) Vector<double>
Generates a Vector with a sequence of count evenly spaced values over an interval between start and stop.
logarithmicSpaced(double start, double stop, {int count = 10, double base = 10.0, bool includeEndpoint = true, DataType<double>? dataType, VectorFormat? format}) Vector<double>
Generates a Vector with a sequence of count evenly spaced values on a log scale (a geometric progression) on the interval between base ^ start and base ^ stop.
lowRegGamma(num a, num x) double
naturalComparable<T extends Comparable<T>>(T a, T b) int
Natural static Comparator function using Comparable arguments.
naturalCompare(Object? a, Object? b) int
Natural dynamic Comparator function.
nearestInterpolation({required Vector<double> xs, required Vector<double> ys, bool preferLower = true}) UnaryFunction<double>
A function providing the nearest value of a discrete monotonically increasing set of sample points xs and ys.
nextInterpolation({required Vector<double> xs, required Vector<double> ys, double right = double.nan}) UnaryFunction<double>
A function providing the next value of a discrete monotonically increasing set of sample points xs and ys. Returns right if there is no next sample point.
permutation(num n, num m) double
Returns the permutations based on the gamma function.
permutationLn(num n, num m) double
Returns the logarithm of the permutations based on the gammaLn function.
previousInterpolation({required Vector<double> xs, required Vector<double> ys, double left = double.nan}) UnaryFunction<double>
A function providing the previous value of a discrete monotonically increasing set of sample points xs and ys. Returns left if there is no previous sample point.
reverseComparable<T extends Comparable<T>>(T a, T b) int
Reversed static Comparator function using Comparable arguments.
reverseCompare(Object? a, Object? b) int
Reversed dynamic Comparator function.
solve(UnaryFunction<double> function, double a, double b, {double bracketEpsilon = 1e-10, double solutionEpsilon = 1e-50, int maxIterations = 50}) double
Returns the root of the provided function bracketed between a and b, that is f(x) = 0 is solved for x in the range of a, b.

Typedefs

UnaryFunction<T> = T Function(T x)
A function with a single argument and an identical return type. Typically used for numerical functions like f(x) where xT and f(x)T.

Exceptions / Errors

IntegrateError
Integration error that is thrown when warnings are not handled explicitly.
InvalidProbability
Error of an invalid probability outside the range of 0 to 1.
LayoutError
Error indicating an unexpected Layout state.