SSJ
V. 2.6.

## umontreal.iro.lecuyer.probdist Class AndersonDarlingDistQuick

```java.lang.Object
umontreal.iro.lecuyer.probdist.ContinuousDistribution
umontreal.iro.lecuyer.probdist.AndersonDarlingDist
umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick
```
All Implemented Interfaces:
Distribution

`public class AndersonDarlingDistQuickextends AndersonDarlingDist`

Extends the class `AndersonDarlingDist` for the distribution (see). This class implements a version faster and more precise in the tails than class `AndersonDarlingDist`.

Field Summary

Fields inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution
`decPrec`

Constructor Summary
`AndersonDarlingDistQuick(int n)`
Constructs an distribution for a sample of size n.

Method Summary
` double` `barF(double x)`
Returns the complementary distribution function.
`static double` ```barF(int n, double x)```
Computes the complementary distribution function bar(F)n(x) with parameter n.
` double` `cdf(double x)`
Returns the distribution function F(x).
`static double` ```cdf(int n, double x)```
Computes the distribution function Fn(x) at x for sample size n.
` double` `density(double x)`
Returns f (x), the density evaluated at x.
`static double` ```density(int n, double x)```
Computes the density of the distribution with parameter n.
` double` `inverseF(double u)`
Returns the inverse distribution function x = F-1(u).
`static double` ```inverseF(int n, double u)```
Computes the inverse x = Fn-1(u) of the distribution with parameter n.
` String` `toString()`

Methods inherited from class umontreal.iro.lecuyer.probdist.AndersonDarlingDist
`getN, getParams, setN`

Methods inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution
`getMean, getStandardDeviation, getVariance, getXinf, getXsup, inverseBisection, inverseBrent, setXinf, setXsup`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Constructor Detail

### AndersonDarlingDistQuick

`public AndersonDarlingDistQuick(int n)`
Constructs an distribution for a sample of size n.

Method Detail

### density

`public double density(double x)`
Description copied from class: `ContinuousDistribution`
Returns f (x), the density evaluated at x.

Overrides:
`density` in class `AndersonDarlingDist`
Parameters:
`x` - value at which the density is evaluated
Returns:
density function evaluated at x

### cdf

`public double cdf(double x)`
Description copied from interface: `Distribution`
Returns the distribution function F(x).

Specified by:
`cdf` in interface `Distribution`
Overrides:
`cdf` in class `AndersonDarlingDist`
Parameters:
`x` - value at which the distribution function is evaluated
Returns:
distribution function evaluated at x

### barF

`public double barF(double x)`
Description copied from class: `ContinuousDistribution`
Returns the complementary distribution function. The default implementation computes bar(F)(x) = 1 - F(x).

Specified by:
`barF` in interface `Distribution`
Overrides:
`barF` in class `AndersonDarlingDist`
Parameters:
`x` - value at which the complementary distribution function is evaluated
Returns:
complementary distribution function evaluated at x

### inverseF

`public double inverseF(double u)`
Description copied from class: `ContinuousDistribution`
Returns the inverse distribution function x = F-1(u). Restrictions: u∈[0, 1].

Specified by:
`inverseF` in interface `Distribution`
Overrides:
`inverseF` in class `AndersonDarlingDist`
Parameters:
`u` - value at which the inverse distribution function is evaluated
Returns:
the inverse distribution function evaluated at u

### density

```public static double density(int n,
double x)```
Computes the density of the distribution with parameter n.

### cdf

```public static double cdf(int n,
double x)```
Computes the distribution function Fn(x) at x for sample size n. For 0.2 < x < 5, the asymptotic distribution F(x) = limn -> ∞Fn(x) was first computed by numerical integration; then a linear correction O(1/n) obtained by simulation was added. For 5 < x, the Grace-Wood empirical approximation is used. For x < 0.2, the Marsaglias' approximation for n = ∞ is used.

For n > 6, the method gives at least 3 decimal digits of precision except for small x; for n <= 6, it gives at least 2 decimal digits of precision except for small x. For n = 1, the exact formula F1(x) = (1 - 4e^-x-1)1/2, for x >= ln(4) - 1, is used.

### barF

```public static double barF(int n,
double x)```
Computes the complementary distribution function bar(F)n(x) with parameter n.

### inverseF

```public static double inverseF(int n,
double u)```
Computes the inverse x = Fn-1(u) of the distribution with parameter n.

### toString

`public String toString()`
Overrides:
`toString` in class `AndersonDarlingDist`

SSJ
V. 2.6.

To submit a bug or ask questions, send an e-mail to Pierre L'Ecuyer.