
Mathematical and statistical functions for the Discrete Uniform distribution, which is commonly used as a discrete variant of the more popular Uniform distribution, used to model events with an equal probability of occurring (e.g. role of a die).
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on
DUnif(lower = 0, upper = 1)
N/A
N/A
distr6::Distribution
-> distr6::SDistribution
-> DiscreteUniform
name
Full name of distribution.
short_name
Short name of distribution for printing.
description
Brief description of the distribution.
packages
Packages required to be installed in order to construct the distribution.
properties
Returns distribution properties, including skewness type and symmetry.
new()
Creates a new instance of this R6 class.
DiscreteUniform$new(lower = NULL, upper = NULL, decorators = NULL)
lower
(integer(1))
Lower limit of the Distribution, defined on the Naturals.
upper
(integer(1))
Upper limit of the Distribution, defined on the Naturals.
decorators
(character())
Decorators to add to the distribution during construction.
mean()
The arithmetic mean of a (discrete) probability distribution X is the expectation
DiscreteUniform$mean(...)
...
Unused.
mode()
The mode of a probability distribution is the point at which the pdf is a local maximum, a distribution can be unimodal (one maximum) or multimodal (several maxima).
DiscreteUniform$mode(which = "all")
which
(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all"
returns all modes, otherwise specifies
which mode to return.
variance()
The variance of a distribution is defined by the formula
DiscreteUniform$variance(...)
...
Unused.
skewness()
The skewness of a distribution is defined by the third standardised moment,
DiscreteUniform$skewness(...)
...
Unused.
kurtosis()
The kurtosis of a distribution is defined by the fourth standardised moment,
DiscreteUniform$kurtosis(excess = TRUE, ...)
excess
(logical(1))
If TRUE
(default) excess kurtosis returned.
...
Unused.
entropy()
The entropy of a (discrete) distribution is defined by
DiscreteUniform$entropy(base = 2, ...)
base
(integer(1))
Base of the entropy logarithm, default = 2 (Shannon entropy)
...
Unused.
mgf()
The moment generating function is defined by
DiscreteUniform$mgf(t, ...)
t
(integer(1))
t integer to evaluate function at.
...
Unused.
cf()
The characteristic function is defined by
DiscreteUniform$cf(t, ...)
t
(integer(1))
t integer to evaluate function at.
...
Unused.
pgf()
The probability generating function is defined by
DiscreteUniform$pgf(z, ...)
z
(integer(1))
z integer to evaluate probability generating function at.
...
Unused.
clone()
The objects of this class are cloneable with this method.
DiscreteUniform$clone(deep = FALSE)
deep
Whether to make a deep clone.
The Discrete Uniform distribution parameterised with lower,
McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.
Other discrete distributions:
Bernoulli
,
Binomial
,
Categorical
,
Degenerate
,
EmpiricalMV
,
Empirical
,
Geometric
,
Hypergeometric
,
Logarithmic
,
Multinomial
,
NegativeBinomial
,
WeightedDiscrete
Other univariate distributions:
Arcsine
,
Bernoulli
,
BetaNoncentral
,
Beta
,
Binomial
,
Categorical
,
Cauchy
,
ChiSquaredNoncentral
,
ChiSquared
,
Degenerate
,
Empirical
,
Erlang
,
Exponential
,
FDistributionNoncentral
,
FDistribution
,
Frechet
,
Gamma
,
Geometric
,
Gompertz
,
Gumbel
,
Hypergeometric
,
InverseGamma
,
Laplace
,
Logarithmic
,
Logistic
,
Loglogistic
,
Lognormal
,
NegativeBinomial
,
Normal
,
Pareto
,
Poisson
,
Rayleigh
,
ShiftedLoglogistic
,
StudentTNoncentral
,
StudentT
,
Triangular
,
Uniform
,
Wald
,
Weibull
,
WeightedDiscrete