Package-level declarations

Provides an implementation of the Bernoulli distribution with success probability (p) P(X=1) = p P(X=0) = 1-p

Create Beta distribution with the supplied parameters

Provides an interface for functions related to a cumulative distribution function CDF

Represents a multi-variate normal distribution with means = 0.0 and the provided covariances. The computed CDF values are to about 2 decimal places using Monte-Carlo integration. There are more efficient and accurate methods to do this computation than done here.

Represents a multi-variate t-distribution with means = 0.0 and the provided covariances. The computed CDF values are to about 2 decimal places using Monte-Carlo integration

Constructs a degenerate distribution with all probability at the provided point. Once made the value of the constant cannot be changed.

Provides a representation for a discrete distribution with arbitrary values and assigned probabilities to each value. Allows the specification of the distribution via a pair of arrays containing the values = {v1, v2, ... , vn} and the cumulative probabilities cdf = {c1, c2, ... , 1.0}

General interface for functions that have probability distributions

Represents the basic interface that probability distributions must implement.

Used to represent the set of possible values for continuous distributions The interval may be infinite

Models discrete random variables that are uniformly distributed over a contiguous range of integers. the lower limit must be < upper limit

Models exponentially distributed random variables This distribution is commonly use to model the time between events

Models random variables that have gamma distribution For more information on the gamma distribution and its related functions, see "Object-Oriented Numerical Methods" by D. Besset

Create Beta distribution with the supplied parameters

The geometric distribution is the probability distribution of the number Y = X − 1 of failures before the first success, supported on the set { 0, 1, 2, 3, ... }, where X is the number of Bernoulli trials needed to get one success.

Provides the inverse cumulative distribution function interface for a CDF

A port to Kotlin of the Java implementation provided in:

Laplace(location, scale) distribution

Logistic(location, scale) distribution

Models the lognormal distribution This distribution is commonly used to model the time of a task

Adds the first order and 2nd order loss functions to the DistributionFunctionIfc.

The number of failures before the rth success in a sequence of independent Bernoulli trials with probability p of success on each trial. The range of this random variable is {0, 1, 2, ....}

Models normally distributed random variables

Represents the probability density function for 1-d continuous distributions

Represents a Pearson Type V distribution, see Law (2007) Simulation Modeling and Analysis, McGraw-Hill, pg 293

Represents a Pearson Type VI distribution, see Law (2007) Simulation Modeling and Analysis, McGraw-Hill, pg 294

Represents the probability mass function for 1-d discrete distributions

Represents a Poisson random variable. A Poisson random variable represents the number of occurrences of an event with time or space.

Represents the 2nd order loss function

Represents a Distribution that has been Shifted (translated to the right) The shift must be >= 0.0

The Student T distribution

Represents the Triangular distribution with parameters - minimum value, maximum value and most likely value

Constructs a truncated distribution based on the provided distribution

Creates a truncated normal distribution over the supplied interval. The supplied mean normalMean and variance normalVariance is the mean of the not truncated normal distribution. The supplied mean must be contained within the supplied interval.

Computes the probability and quantile that the studentized range, each based on n means and with df degrees of freedom

Defines a uniform distribution over the given range.

Defines an interface for getting the variance of a distribution

This class defines a Weibull distribution