## Types in MathNet.Numerics.Statistics

Type SortedArrayStatistics

Namespace MathNet.Numerics.Statistics

Statistics operating on an array already sorted ascendingly.

### Public Static Functions

#### doubleEmpiricalCDF(Single[] data, float x)

Estimates the empirical cumulative distribution function (CDF) at x from the sorted data array (ascending).
##### Parameters
###### `Single[]` data

The data sample sequence.

###### `float` x

The value where to estimate the CDF at.

#### doubleEmpiricalCDF(Double[] data, double x)

Estimates the empirical cumulative distribution function (CDF) at x from the sorted data array (ascending).
##### Parameters
###### `Double[]` data

The data sample sequence.

###### `double` x

The value where to estimate the CDF at.

#### Double[]FiveNumberSummary(Double[] data)

Estimates {min, lower-quantile, median, upper-quantile, max} from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### Single[]FiveNumberSummary(Single[] data)

Estimates {min, lower-quantile, median, upper-quantile, max} from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

#### doubleInterquartileRange(Double[] data)

Estimates the inter-quartile range from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### floatInterquartileRange(Single[] data)

Estimates the inter-quartile range from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

#### doubleLowerQuartile(Double[] data)

Estimates the first quartile value from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### floatLowerQuartile(Single[] data)

Estimates the first quartile value from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

#### floatMaximum(Single[] data)

Returns the largest value from the sorted data array (ascending).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

#### doubleMaximum(Double[] data)

Returns the largest value from the sorted data array (ascending).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### doubleMedian(Double[] data)

Estimates the median value from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### floatMedian(Single[] data)

Estimates the median value from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

#### doubleMinimum(Double[] data)

Returns the smallest value from the sorted data array (ascending).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### floatMinimum(Single[] data)

Returns the smallest value from the sorted data array (ascending).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

#### floatOrderStatistic(Single[] data, int order)

Returns the order statistic (order 1..N) from the sorted data array (ascending).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

###### `int` order

One-based order of the statistic, must be between 1 and N (inclusive).

#### doubleOrderStatistic(Double[] data, int order)

Returns the order statistic (order 1..N) from the sorted data array (ascending).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

###### `int` order

One-based order of the statistic, must be between 1 and N (inclusive).

#### floatPercentile(Single[] data, int p)

Estimates the p-Percentile value from the sorted data array (ascending). If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

###### `int` p

Percentile selector, between 0 and 100 (inclusive).

#### doublePercentile(Double[] data, int p)

Estimates the p-Percentile value from the sorted data array (ascending). If a non-integer Percentile is needed, use Quantile instead. Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

###### `int` p

Percentile selector, between 0 and 100 (inclusive).

#### floatQuantile(Single[] data, double tau)

Estimates the tau-th quantile from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8).
R-8, SciPy-(1/3,1/3): Linear interpolation of the approximate medians for order statistics. When tau < (2/3) / (N + 1/3), use x1. When tau >= (N - 1/3) / (N + 1/3), use xN.
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

###### `double` tau

Quantile selector, between 0.0 and 1.0 (inclusive).

#### doubleQuantile(Double[] data, double tau)

Estimates the tau-th quantile from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. Approximately median-unbiased regardless of the sample distribution (R8).
R-8, SciPy-(1/3,1/3): Linear interpolation of the approximate medians for order statistics. When tau < (2/3) / (N + 1/3), use x1. When tau >= (N - 1/3) / (N + 1/3), use xN.
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

###### `double` tau

Quantile selector, between 0.0 and 1.0 (inclusive).

#### doubleQuantileCustom(Double[] data, double tau, QuantileDefinition definition)

Estimates the tau-th quantile from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

###### `double` tau

Quantile selector, between 0.0 and 1.0 (inclusive).

###### `QuantileDefinition` definition

Quantile definition, to choose what product/definition it should be consistent with

#### doubleQuantileCustom(Double[] data, double tau, double a, double b, double c, double d)

Estimates the tau-th quantile from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified by 4 parameters a, b, c and d, consistent with Mathematica.
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

###### `double` tau

Quantile selector, between 0.0 and 1.0 (inclusive).

a-parameter

b-parameter

c-parameter

d-parameter

#### floatQuantileCustom(Single[] data, double tau, double a, double b, double c, double d)

Estimates the tau-th quantile from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified by 4 parameters a, b, c and d, consistent with Mathematica.
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

###### `double` tau

Quantile selector, between 0.0 and 1.0 (inclusive).

a-parameter

b-parameter

c-parameter

d-parameter

#### floatQuantileCustom(Single[] data, double tau, QuantileDefinition definition)

Estimates the tau-th quantile from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.

###### `double` tau

Quantile selector, between 0.0 and 1.0 (inclusive).

###### `QuantileDefinition` definition

Quantile definition, to choose what product/definition it should be consistent with

#### doubleQuantileRank(Double[] data, double x, RankDefinition definition)

Estimates the quantile tau from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
##### Parameters
###### `Double[]` data

The data sample sequence.

Quantile value.

###### `RankDefinition` definition

Rank definition, to choose how ties should be handled and what product/definition it should be consistent with

#### doubleQuantileRank(Single[] data, float x, RankDefinition definition)

Estimates the quantile tau from the sorted data array (ascending). The tau-th quantile is the data value where the cumulative distribution function crosses tau. The quantile definition can be specified to be compatible with an existing system.
##### Parameters
###### `Single[]` data

The data sample sequence.

Quantile value.

###### `RankDefinition` definition

Rank definition, to choose how ties should be handled and what product/definition it should be consistent with

#### Double[]Ranks(Double[] data, RankDefinition definition)

Evaluates the rank of each entry of the sorted data array (ascending). The rank definition can be specified to be compatible with an existing system.

#### Double[]Ranks(Single[] data, RankDefinition definition)

Evaluates the rank of each entry of the sorted data array (ascending). The rank definition can be specified to be compatible with an existing system.

#### doubleUpperQuartile(Double[] data)

Estimates the third quartile value from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Double[]` data

Sample array, must be sorted ascendingly.

#### floatUpperQuartile(Single[] data)

Estimates the third quartile value from the sorted data array (ascending). Approximately median-unbiased regardless of the sample distribution (R8).
##### Parameters
###### `Single[]` data

Sample array, must be sorted ascendingly.