random

Type Members

case class Bernoulli(p: Double = .5, stream: Int = 0) extends Variate with Product with Serializable

This class generates Bernoulli random variates.
This class generates Bernoulli random variates. This discrete RV models the one trial (success is 1, failure is 0).
p
the probability of success
stream
the random number stream

See also
http://www.math.uah.edu/stat/bernoulli/Introduction.html
case class Beta(alpha: Double = 2, beta: Double = 3, stream: Int = 0) extends Variate with Product with Serializable

This class generates Beta random variates.
This class generates Beta random variates. This continuous RV models random proportions. Beta = Gamma1 / (Gamma1 + Gamma2).
alpha
the shape parameter for Gamma1
beta
the shape parameter for Gamma2
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Beta.html
case class Binomial(p: Double = .5, n: Int = 10, stream: Int = 0) extends Variate with Product with Serializable

This class generates Binomial random variates.
This class generates Binomial random variates. This discrete RV models the number of successes in n trials.
p
the probability of success
n
the number of independent trials
stream
the random number stream

See also
http://www.math.uah.edu/stat/bernoulli/Binomial.html
case class Cauchy(alpha: Double = 2.5, beta: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Cauchy (or Lorentz) random variates.
This class generates Cauchy (or Lorentz) random variates. This continuous RV models data with heavier tails than normally distributed.
alpha
the location parameter (median)
beta
the scale parameter
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Cauchy.html
case class ChiSquare(df: Int = 2, stream: Int = 0) extends Variate with Product with Serializable

This class generates ChiSquare random variates.
This class generates ChiSquare random variates. This continuous RV models the variance of a distribution. http://www.math.uah.edu/stat/special/ChiSquare.html
df
the degrees of freedom
stream
the random number stream
case class Dice(cdf: Array[Double] = Array (.1, .3, .5, .7, .9, 1.0), stream: Int = 0) extends Variate with Product with Serializable

This class generates Dice random variates for a given distribution specified using a cumulative distribution function (cdf).
This class generates Dice random variates for a given distribution specified using a cumulative distribution function (cdf). This discrete RV models the roll of dice numbered 0, 1, ..., n-1. Add 1 for 1 to n.
cdf
the distribution function (cdf)
stream
the random number stream
case class Discrete(dist: VectorD = VectorD (.2, .2, .2, .2, .2), x: VectorD = null, cumulative: Boolean = false, stream: Int = 0) extends Variate with Product with Serializable

This class generates generalized Discrete random variates for a given distribution specified using either a probability mass function (pmf) or a cumulative distribution function (cdf).
This class generates generalized Discrete random variates for a given distribution specified using either a probability mass function (pmf) or a cumulative distribution function (cdf). This discrete RV models arbitrary experiments with discrete outcomes.
dist
the distribution function (pdf or cdf)
x
the x-coordinate values (mass points)
cumulative
whether dist is cumulative (cdf) or not (pmf)
stream
the random number stream
case class Erlang(mu: Double = 1.0, k: Int = 2, stream: Int = 0) extends Variate with Product with Serializable

This class generates Erlang random variates.
This class generates Erlang random variates. This continuous RV models the time until k stages complete.
mu
the mean of exponential samples (Erlang mean = mu * k)
k
the number of stages (or Exponential samples)
stream
the random number stream

See also
http://www.math.uah.edu/stat/poisson/Gamma.html
case class Exponential(mu: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Exponential random variates.
This class generates Exponential random variates. This continuous RV models the time until an event occurs. http://www.math.uah.edu/stat/poisson/Exponential.html
mu
the mean
stream
the random number stream
case class Fisher(df1: Int = 6, df2: Int = 4, stream: Int = 0) extends Variate with Product with Serializable

This class generates Fisher (F-Distribution) random variates.
This class generates Fisher (F-Distribution) random variates. This continuous RV models the ratio of variances.
df1
the degrees of freedom for numerator Chi-Square
df2
the degrees of freedom for denominator Chi-Square
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Fisher.html
case class Gamma(alpha: Double = 1.0, beta: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Gamma random variates.
This class generates Gamma random variates. This continuous RV models the time until an event occurs. Note: variance = alpha * beta ^ 2.
alpha
the shape parameter
beta
the scale parameter
stream
the random number stream

See also
http://www.math.uah.edu/stat/poisson/Gamma.html
case class Geometric(p: Double = .5, stream: Int = 0) extends Variate with Product with Serializable

This class generates Geometric random variates.
This class generates Geometric random variates. This discrete RV models the number of failures before the first success.
p
the probability of success
stream
the random number stream

See also
http://www.math.uah.edu/stat/bernoulli/Geometric.html
case class HyperExponential(p: Double = .5, mu1: Double = 1, mu2: Double = 2, stream: Int = 0) extends Variate with Product with Serializable

This class generates HyperExponential random variates (two rates).
This class generates HyperExponential random variates (two rates). This continuous RV models the time until an event occurs (higher coefficient of variation than exponential distribution).
p
the probability of first vs. second rates
mu1
the first mean (1 / lambda1)
mu2
the second mean (1 / lambda2)
stream
the random number stream
case class HyperGeometric(p: Double = .5, n: Int = 5, pop: Int = 10, stream: Int = 0) extends Variate with Product with Serializable

This class generates HyperGeometric random variates.
This class generates HyperGeometric random variates. This discrete RV models the number of successes in n draws from a finite population.
p
the probability of success (red balls)
n
the number of draws (balls drawn)
pop
the size of the finite population (total number of balls)
stream
the random number stream

See also
http://www.math.uah.edu/stat/urn/Hypergeometric.html
case class LogNormal(mu: Double = 0.0, sigma2: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates LogNormal random variates.
This class generates LogNormal random variates. This continuous RV models data that is normally distributed after a log transformation.
mu
the mean for underlying Normal
sigma2
the variance (sigma squared) for underlying Normal
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/LogNormal.html
case class Logistic(a: Double = 0.0, b: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Logistic random variates.
This class generates Logistic random variates. This continuous RV models logisticlly distributed data (stretched Normal).
a
the location parameter
b
the scale parameter
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Logistic.html
case class Multinomial(p: Array[Double] = Array (.4, .3, .3), n: Int = 5, stream: Int = 0) extends Variate with Product with Serializable

This class generates Multinomial random variates vectors.
This class generates Multinomial random variates vectors. This discrete RV models the ...?
p
array of probabilities
n
the number of independent trials
stream
the random number stream

See also
http://www.math.uah.edu/stat/bernoulli/Multinomial.html
case class NHPoissonProcess(lambda: VectorD, dt: Double = 1.0, stream: Int = 0) extends TimeVariate with Product with Serializable

This class generates arrival times according to a NHPoissonProces, an Non-Homogeneous Process Process (NHPP), where the arrival rate function 'lambda(t)' is piecewise constant.
This class generates arrival times according to a NHPoissonProces, an Non-Homogeneous Process Process (NHPP), where the arrival rate function 'lambda(t)' is piecewise constant. Rates are constant over basic time intervals of length 'dt'.
lambda
the vector of arrival rates
dt
the length the basic time intervals
stream
the random number stream

See also
http://en.wikipedia.org/wiki/Poisson_process#Non-homogeneous
case class NegativeBinomial(p: Double = .5, s: Int = 2, stream: Int = 0) extends Variate with Product with Serializable

This class generates NegativeBinomial random variates.
This class generates NegativeBinomial random variates. This discrete RV models the number of failures before s-th success.
p
the probability of success
s
the number of successes
stream
the random number stream

See also
http://www.math.uah.edu/stat/bernoulli/NegativeBinomial.html
case class Normal(mu: Double = 0.0, sigma2: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Normal (Gaussian) random variates.
This class generates Normal (Gaussian) random variates. This continuous RV models normally distributed data (bell curve). When summed, most distributions tend to Normal (Central Limit Theorem).
mu
the mean
sigma2
the variance (sigma squared)
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Normal.html
case class NormalVec(mu: VectorD, cov: MatrixD, stream: Int = 0) extends VariateVec with Product with Serializable

The NormalVec class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with mean 'mu' and covariance 'cov'.
The NormalVec class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with mean 'mu' and covariance 'cov'. This continuous RVV models normally distributed multidimensional data.
mu
the mean vector
cov
the covariance matrix
stream
the random number stream

See also
http://www.statlect.com/mcdnrm1.htm
http://onlinelibrary.wiley.com/doi/10.1111/1467-9639.00037/pdf
case class Pareto(a: Double = 1.0, b: Double = 0.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Pareto random variates.
This class generates Pareto random variates. This continuous RV models Pareto distributed data.
a
the shape parameter
b
the scale parameter
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Pareto.html
case class PermutedVecD(x: VectorD, stream: Int = 0) extends VariateVec with Product with Serializable

The PermutedVecD class generates random permutations of a vector of doubles.
The PermutedVecD class generates random permutations of a vector of doubles.
x
the vector of doubles to permute
stream
the random number stream

See also
maths-people.anu.edu.au/~brent/pd/Arndt-thesis.pdf
case class PermutedVecI(x: VectorI, stream: Int = 0) extends VariateVec with Product with Serializable

The PermutedVecI class generates random permutations of a vector of integers.
The PermutedVecI class generates random permutations of a vector of integers.
x
the vector of integers to permute
stream
the random number stream

See also
maths-people.anu.edu.au/~brent/pd/Arndt-thesis.pdf
case class Poisson(mu: Double = 2.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Poisson random variates (discrete).
This class generates Poisson random variates (discrete). This discrete RV models the number of events in a time interval of unit length.
mu
the mean
stream
the random number stream

See also
http://www.math.uah.edu/stat/poisson/Poisson.html
case class PoissonProcess(lambda: Double, stream: Int = 0) extends TimeVariate with Product with Serializable

This class generates arrival times according to a PoissonProcess.
This class generates arrival times according to a PoissonProcess. Given the current arrival time 't', generate the next arrival time.
lambda
the arrival rate (arrivals per unit time)
stream
the random number stream

See also
http://en.wikipedia.org/wiki/Poisson_process
case class ProbabilityVec(n: Int, d: Double = 0.5, stream: Int = 0) extends VariateVec with Product with Serializable

The ProbabilityVec class generates a probability vector where the ith probability is '1/n' with a +/- randomizing displacement of at most 'd'.
The ProbabilityVec class generates a probability vector where the ith probability is '1/n' with a +/- randomizing displacement of at most 'd'. Note, the probability vector must add to one.
n
the dimension/size of the probability vector
d
the randomizing displacement, must be in [0, 1]
abstract class RNG extends Error

The RNG abstract class is the base class for all ScalaTion Random Number Generators (RNGs).
The RNG abstract class is the base class for all ScalaTion Random Number Generators (RNGs). The subclasses must implement a 'gen' method that generates random real numbers in the range (0, 1). They must also implement an 'igen' methods to return stream values.
case class Randi(a: Int = 0, b: Int = 5, stream: Int = 0) extends Variate with Product with Serializable

This class generates Randi random variates (random integers: a, ..., b).
This class generates Randi random variates (random integers: a, ..., b). This discrete RV models equiprobable integral outcomes.
a
the lower bound (inclusive)
b
the upper bound (inclusive)
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/UniformDiscrete.html
case class Randi0(b: Int = 5, stream: Int = 0) extends Variate with Product with Serializable

This class generates Randi0 random variates (random integers: 0, ..., b).
This class generates Randi0 random variates (random integers: 0, ..., b). This discrete RV models equiprobable integral outcomes starting with 0.
b
the upper bound (>= 0) (inclusive)
stream
the random number stream
case class Random(stream: Int = 0) extends RNG with Product with Serializable

The Random class generates random real numbers in the range (0, 1).
The Random class generates random real numbers in the range (0, 1). It implements, using 64-bit integers (Long's), the 'MRG31k3p' generator developed by L'Ecuyer and Touzin, described in "FAST COMBINED MULTIPLE RECURSIVE GENERATORS WITH MULTIPLIERS OF THE FORM a = 2^{q +/- 2}r". MRG31k3p is a Combined Multiple Recursive Generator (CMRG) shown to have good performance and statistical properties for simulations. It has a period of about 2^{185 and is considered to be a faster alternative to the popular
'MRG32k3' generator. MRG31k3p combines MRG1 and MRG2.

MRG1: x_i = (0 + a_12 x_i-2 + a_13 x_i-3) % M1
MRG2: x_i = (a_21 x_i-1 + 0 + a_23 x_i-3) % M2

where a_12 = 2}22, a_13 = 2^{7+1, a_21 = 2}15 and a_23 = 2^15+1.
stream
the random number stream index

See also
http://www.iro.umontreal.ca/~simardr/ssj/doc/pdf/guiderng.pdf
http://www.informs-sim.org/wsc00papers/090.PDF
case class Random2(stream: Int = 0) extends RNG with Product with Serializable

The Random2 class generates random real numbers in the range (0, 1).
The Random2 class generates random real numbers in the range (0, 1). It implements, using 32-bit integers (Int's), the 'MRG31k3p' generator developed by L'Ecuyer and Touzin, described in "FAST COMBINED MULTIPLE RECURSIVE GENERATORS WITH MULTIPLIERS OF THE FORM a = 2^{q +/- 2}r". MRG31k3p is a Combined Multiple Recursive Generator (CMRG) shown to have good performance and statistical properties for simulations. It has a period of about 2^{185 and is considered to be a faster alternative to the popular
'MRG32k3' generator. MRG31k3p combines MRG1 and MRG2.

MRG1: x_i = (0 + a_12 x_i-2 + a_13 x_i-3) % M1
MRG2: x_i = (a_21 x_i-1 + 0 + a_23 x_i-3) % M2

where a_12 = 2}22, a_13 = 2^{7+1, a_21 = 2}15 and a_23 = 2^15+1.
stream
the random number stream index

See also
http://www.iro.umontreal.ca/~simardr/ssj/doc/pdf/guiderng.pdf
http://www.informs-sim.org/wsc00papers/090.PDF
case class Random3(stream: Int = 0) extends RNG with Product with Serializable

The Random3 class generates random real numbers in the range (0, 1).
The Random3 class generates random real numbers in the range (0, 1). It implements, using 64-bit integers (Int's), the 'MINSTD' generator, which is a multiplicative Linear Congruential Generator (LCG). These generators were commonly used in the last century.
x_i = a x_i-1 % m
stream
the random number stream index

See also
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.1024
Random
http://random.mat.sbg.ac.at/results/karl/server/node4.html#SECTION00042000000000000000 In case a better generator is needed, a Multiple Recursive Generator (MRG) or Composite Multiple Recursive Generator (CMRG) should be used.
case class Sharp(x: Double = 1, stream: Int = 0) extends Variate with Product with Serializable

This class generates Sharp (Deterministic) random variates.
This class generates Sharp (Deterministic) random variates. This discrete RV models the case when the variance is 0.
x
the value for this constant distribution
stream
the random number stream
case class StudentT(df: Int = 4, stream: Int = 0) extends Variate with Product with Serializable

This class generates StudentT random variates.
This class generates StudentT random variates. This continuous RV models cases where data are normally distributed, but variability increases since the variance is unknown.
df
the degrees of freedom
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Student.html
abstract class TimeVariate extends Variate

The TimeVariate abstract class serves as a superclass for time-based random variates such Poisson Processes.
case class Trapezoidal(a: Double = 0.0, c: Double = 2.0, d: Double = 7.0, b: Double = 10.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Trapezoidal random variates.
This class generates Trapezoidal random variates. This continuous RV models cases where outcomes cluster between two modes. Both Uniform and Triangular are special cases of Trapezoidal.
a
the minimum
c
the first mode
d
the second mode
b
the maximum
stream
the random number stream

See also
http://iopscience.iop.org/0026-1394/44/2/003/pdf/0026-1394_44_2_003.pdf
case class Triangular(a: Double = 0, b: Double = 5, c: Double = Double.MaxValue, stream: Int = 0) extends Variate with Product with Serializable

This class generates simple Triangular random variates with the mode in the middle.
This class generates simple Triangular random variates with the mode in the middle. This continuous RV models cases where outcomes cluster around the mode.
a
the lower bound
b
the upper bound
c
the mode
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Triangle.html
case class Trinomial(p: Double = 1.0/3.0, q: Double = 1.0/3.0, n: Int = 10, stream: Int = 0) extends Variate with Product with Serializable

This class generates Trinomial random variates.
This class generates Trinomial random variates. While Binomial is based on trials with two outcomes, success (1) or failure (0). Trinomial is based on trials with three outcomes, high (2), medium (1) or low (0). This discrete RV models the result of 'n' trials.
p
the probability of high (2)
q
the probability of medium (1)
n
the number of independent trials
stream
the random number stream

See also
https://onlinecourses.science.psu.edu/stat414/node/106
case class Uniform(a: Double = 0.0, b: Double = 5.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Uniform random variates in the range (a, b).
This class generates Uniform random variates in the range (a, b). This continuous RV models equiprobable outcomes.
a
the lower bound
b
the upper bound
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/UniformContinuous.html
abstract class Variate extends Error

The Variate abstract class serves as a base class for all the random variate (RV) generators.
The Variate abstract class serves as a base class for all the random variate (RV) generators. They use one of the Random Number Generators (RNG's) from Random to generate numbers following their particular distribution. Random Variate Generators (RVG's) for thirty popular probability distributions are implemented as extensions of Variate. Still need to add one.

See also
VariateVec for Random MultiVariate Generators (RMVG's).
http://www.math.uah.edu/stat/special/index.html
abstract class VariateVec extends Error

The VariateVec abstract class serves as a base class for all the random variate vector (RVV) generators.
The VariateVec abstract class serves as a base class for all the random variate vector (RVV) generators. They use one of the Random Number Generators (RNG's) from Random.scala to generate numbers following their particular multivariate distribution.
case class Weibull(alpha: Double = 2.0, beta: Double = 2.0, stream: Int = 0) extends Variate with Product with Serializable

This class generates Weibull random variates.
This class generates Weibull random variates. This continuous RV models the time for an event to occur.
alpha
the shape parameter
beta
the scale parameter
stream
the random number stream

See also
http://www.math.uah.edu/stat/special/Weibull.html
case class _HyperExponential(mu: Double = 1.0, sigma: Double = 2, stream: Int = 0) extends Variate with Product with Serializable

This class generates HyperExponential random variates.
This class generates HyperExponential random variates. This continuous RV models the time until an event occurs (higher coefficient of variation than exponential distribution). FIX
mu
the mean
sigma
the standard deviation
stream
the random number stream

Value Members

object PoissonProcessTest extends App

The PoissonProcessTest object is used to test both the PoissonProcess and NHPoissonProcess classes.
object Quantile extends Error

The Quantile object contains methods for computing 'Finv', the inverse Cumulative Distribution Functions (iCDF's) for popular sampling distributions: StandardNormal, StudentT, ChiSquare and Fisher.
The Quantile object contains methods for computing 'Finv', the inverse Cumulative Distribution Functions (iCDF's) for popular sampling distributions: StandardNormal, StudentT, ChiSquare and Fisher. For a given CDF 'F' and quantile 'p', compute 'x' such that the 'F(x) = p'.
object QuantileTest extends App

The QuantileTest object tests the Quantile object.
object RNGTest extends App with Error

The RNGTest object conducts three simple tests of the Random Number Generators: (1) Spped Test, (2) Means Test and (3) Chi-square Goodness of Fit Test.
The RNGTest object conducts three simple tests of the Random Number Generators: (1) Spped Test, (2) Means Test and (3) Chi-square Goodness of Fit Test. FIX: need to add (3) Variance Test and (4) K-S Goodness of Fit Test.
object RandomSeeds

The first 1000 seeds for the 'MRG31k3p' random number generator.
object RandomSeeds3

The first 1000 seeds for the LCG random number generator.
object StreamMaker

The StreamMaker object computes seeds for Random and Random2, both of which implement the 'MRG31k3p' random number generator.
The StreamMaker object computes seeds for Random and Random2, both of which implement the 'MRG31k3p' random number generator. This generator has a period length around 2^185. Each seed is a 6-dimensional vector of 32-bit integers.

See also
http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
object StreamMaker3 extends App

The StreamMaker3 object finds seeds for the Random3 random number generator.
The StreamMaker3 object finds seeds for the Random3 random number generator. This generator has a period length around 2^31. Each seed is a 32-bit integer.
object StreamMakerGen extends App

The StreamMakerGen object generates and prints the first 'k' seeds for the the 'MRG31k3p' random number generator's streams.
object VariateTest extends App

The VariateTest object conducts two simple tests of the Random Variate Generators: (1) Means Test and (2) Chi-square Goodness of Fit Test.
The VariateTest object conducts two simple tests of the Random Variate Generators: (1) Means Test and (2) Chi-square Goodness of Fit Test. FIX: need to add (3) Variance Test and (4) K-S Goodness of Fit Test.
object VariateVecTest extends App

The VariateVecTest object is used to test the Random Variate Vector (RVV) generators from the classes derived from VariateVec.

package random

Type Members

case class Bernoulli(p: Double = .5, stream: Int = 0) extends Variate with Product with Serializable

case class Beta(alpha: Double = 2, beta: Double = 3, stream: Int = 0) extends Variate with Product with Serializable

case class Binomial(p: Double = .5, n: Int = 10, stream: Int = 0) extends Variate with Product with Serializable

case class Cauchy(alpha: Double = 2.5, beta: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

case class ChiSquare(df: Int = 2, stream: Int = 0) extends Variate with Product with Serializable

case class Dice(cdf: Array[Double] = Array (.1, .3, .5, .7, .9, 1.0), stream: Int = 0) extends Variate with Product with Serializable

case class Discrete(dist: VectorD = VectorD (.2, .2, .2, .2, .2), x: VectorD = null, cumulative: Boolean = false, stream: Int = 0) extends Variate with Product with Serializable

case class Erlang(mu: Double = 1.0, k: Int = 2, stream: Int = 0) extends Variate with Product with Serializable

case class Exponential(mu: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

case class Fisher(df1: Int = 6, df2: Int = 4, stream: Int = 0) extends Variate with Product with Serializable

case class Gamma(alpha: Double = 1.0, beta: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

case class Geometric(p: Double = .5, stream: Int = 0) extends Variate with Product with Serializable

case class HyperExponential(p: Double = .5, mu1: Double = 1, mu2: Double = 2, stream: Int = 0) extends Variate with Product with Serializable

case class HyperGeometric(p: Double = .5, n: Int = 5, pop: Int = 10, stream: Int = 0) extends Variate with Product with Serializable

case class LogNormal(mu: Double = 0.0, sigma2: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

case class Logistic(a: Double = 0.0, b: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

case class Multinomial(p: Array[Double] = Array (.4, .3, .3), n: Int = 5, stream: Int = 0) extends Variate with Product with Serializable

case class NHPoissonProcess(lambda: VectorD, dt: Double = 1.0, stream: Int = 0) extends TimeVariate with Product with Serializable

case class NegativeBinomial(p: Double = .5, s: Int = 2, stream: Int = 0) extends Variate with Product with Serializable

case class Normal(mu: Double = 0.0, sigma2: Double = 1.0, stream: Int = 0) extends Variate with Product with Serializable

case class NormalVec(mu: VectorD, cov: MatrixD, stream: Int = 0) extends VariateVec with Product with Serializable

case class Pareto(a: Double = 1.0, b: Double = 0.0, stream: Int = 0) extends Variate with Product with Serializable

case class PermutedVecD(x: VectorD, stream: Int = 0) extends VariateVec with Product with Serializable

case class PermutedVecI(x: VectorI, stream: Int = 0) extends VariateVec with Product with Serializable

case class Poisson(mu: Double = 2.0, stream: Int = 0) extends Variate with Product with Serializable

case class PoissonProcess(lambda: Double, stream: Int = 0) extends TimeVariate with Product with Serializable

case class ProbabilityVec(n: Int, d: Double = 0.5, stream: Int = 0) extends VariateVec with Product with Serializable

abstract class RNG extends Error

case class Randi(a: Int = 0, b: Int = 5, stream: Int = 0) extends Variate with Product with Serializable

case class Randi0(b: Int = 5, stream: Int = 0) extends Variate with Product with Serializable

case class Random(stream: Int = 0) extends RNG with Product with Serializable

case class Random2(stream: Int = 0) extends RNG with Product with Serializable

case class Random3(stream: Int = 0) extends RNG with Product with Serializable

case class Sharp(x: Double = 1, stream: Int = 0) extends Variate with Product with Serializable

case class StudentT(df: Int = 4, stream: Int = 0) extends Variate with Product with Serializable

abstract class TimeVariate extends Variate

case class Trapezoidal(a: Double = 0.0, c: Double = 2.0, d: Double = 7.0, b: Double = 10.0, stream: Int = 0) extends Variate with Product with Serializable

case class Triangular(a: Double = 0, b: Double = 5, c: Double = Double.MaxValue, stream: Int = 0) extends Variate with Product with Serializable

case class Trinomial(p: Double = 1.0/3.0, q: Double = 1.0/3.0, n: Int = 10, stream: Int = 0) extends Variate with Product with Serializable

case class Uniform(a: Double = 0.0, b: Double = 5.0, stream: Int = 0) extends Variate with Product with Serializable

abstract class Variate extends Error

abstract class VariateVec extends Error

case class Weibull(alpha: Double = 2.0, beta: Double = 2.0, stream: Int = 0) extends Variate with Product with Serializable

case class _HyperExponential(mu: Double = 1.0, sigma: Double = 2, stream: Int = 0) extends Variate with Product with Serializable

Value Members

object PoissonProcessTest extends App

object Quantile extends Error

object QuantileTest extends App

object RNGTest extends App with Error

object RandomSeeds

object RandomSeeds3

object StreamMaker

object StreamMaker3 extends App

object StreamMakerGen extends App

object VariateTest extends App

object VariateVecTest extends App

Inherited from AnyRef

Inherited from Any

Ungrouped