Jackknife<T> class

A deterministic resampling technique to estimate variance, bias, and confidence intervals.

For details see https://en.wikipedia.org/wiki/Jackknife_resampling.

Constructors

Jackknife(List<T> samples, double statistic(List<T> list), {double confidenceLevel = 0.95})

Properties

bias → double

The bias.

late, final

confidenceLevel → double

The confidence level for the confidence interval.

final

defaultToStringPrinter → ObjectPrinter

Override to configure the empty ObjectPrinter.

no setter, inherited

estimate → double

The bias corrected estimate.

late, final

hashCode → int

The hash code for this object.

no setter, inherited

lowerBound → double

The lower bound of the confidence interval.

late, final

resamples → List<List<T>>

The resamples of the data.

late, final

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

samples → List<T>

The sample data.

final

standardError → double

The standard error.

late, final

statistic → double Function(List<T> list)

The statistical function to measure.

final

toStringPrinter → ObjectPrinter

Override and call super to add values to the ObjectPrinter.

no setter

upperBound → double

The upper bound of the confidence interval.

late, final

Methods

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

toString() → String

Standard toString implementation. Do not override, instead implement toStringPrinter to customize.

inherited

Operators

operator ==(Object other) → bool

The equality operator.

inherited