Description Details Value Distribution support Default Parameterisation Omitted Methods Also known as Super classes Public fields Active bindings Methods Author(s) References See Also

Mathematical and statistical functions for the Uniform distribution, which is commonly used to model continuous events occurring with equal probability, as an uninformed prior in Bayesian modelling, and for inverse transform sampling.

The Uniform distribution parameterised with lower, *a*, and upper, *b*, limits is defined by the pdf,

*f(x) = 1/(b-a)*

for *-∞ < a < b < ∞*.

Returns an R6 object inheriting from class SDistribution.

The distribution is supported on *[a, b]*.

Unif(lower = 0, upper = 1)

N/A

N/A

`distr6::Distribution`

-> `distr6::SDistribution`

-> `Uniform`

`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.

Uniform$new(lower = NULL, upper = NULL, decorators = NULL)

`lower`

`(numeric(1))`

Lower limit of the Distribution, defined on the Reals.`upper`

`(numeric(1))`

Upper limit of the Distribution, defined on the Reals.`decorators`

`(character())`

Decorators to add to the distribution during construction.

`mean()`

The arithmetic mean of a (discrete) probability distribution X is the expectation

*E_X(X) = ∑ p_X(x)*x*

with an integration analogue for continuous distributions.

Uniform$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).

Uniform$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

*var_X = E[X^2] - E[X]^2*

where *E_X* is the expectation of distribution X. If the distribution is multivariate the
covariance matrix is returned.

Uniform$variance(...)

`...`

Unused.

`skewness()`

The skewness of a distribution is defined by the third standardised moment,

*sk_X = E_X[((x - μ)/σ)^3]*

where *E_X* is the expectation of distribution X, *μ* is the mean of the
distribution and *σ* is the standard deviation of the distribution.

Uniform$skewness(...)

`...`

Unused.

`kurtosis()`

The kurtosis of a distribution is defined by the fourth standardised moment,

*k_X = E_X[((x - μ)/σ)^4]*

where *E_X* is the expectation of distribution X, *μ* is the mean of the
distribution and *σ* is the standard deviation of the distribution.
Excess Kurtosis is Kurtosis - 3.

Uniform$kurtosis(excess = TRUE, ...)

`excess`

`(logical(1))`

If`TRUE`

(default) excess kurtosis returned.`...`

Unused.

`entropy()`

The entropy of a (discrete) distribution is defined by

*- ∑ (f_X)log(f_X)*

where *f_X* is the pdf of distribution X, with an integration analogue for
continuous distributions.

Uniform$entropy(base = 2, ...)

`base`

`(integer(1))`

Base of the entropy logarithm, default = 2 (Shannon entropy)`...`

Unused.

`mgf()`

The moment generating function is defined by

*mgf_X(t) = E_X[exp(xt)]*

where X is the distribution and *E_X* is the expectation of the distribution X.

Uniform$mgf(t, ...)

`t`

`(integer(1))`

t integer to evaluate function at.`...`

Unused.

`cf()`

The characteristic function is defined by

*cf_X(t) = E_X[exp(xti)]*

where X is the distribution and *E_X* is the expectation of the distribution X.

Uniform$cf(t, ...)

`t`

`(integer(1))`

t integer to evaluate function at.`...`

Unused.

`pgf()`

The probability generating function is defined by

*pgf_X(z) = E_X[exp(z^x)]*

where X is the distribution and *E_X* is the expectation of the distribution X.

Uniform$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.

Uniform$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

Yumi Zhou

McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.

Other continuous distributions:
`Arcsine`

,
`BetaNoncentral`

,
`Beta`

,
`Cauchy`

,
`ChiSquaredNoncentral`

,
`ChiSquared`

,
`Dirichlet`

,
`Erlang`

,
`Exponential`

,
`FDistributionNoncentral`

,
`FDistribution`

,
`Frechet`

,
`Gamma`

,
`Gompertz`

,
`Gumbel`

,
`InverseGamma`

,
`Laplace`

,
`Logistic`

,
`Loglogistic`

,
`Lognormal`

,
`MultivariateNormal`

,
`Normal`

,
`Pareto`

,
`Poisson`

,
`Rayleigh`

,
`ShiftedLoglogistic`

,
`StudentTNoncentral`

,
`StudentT`

,
`Triangular`

,
`Wald`

,
`Weibull`

Other univariate distributions:
`Arcsine`

,
`Bernoulli`

,
`BetaNoncentral`

,
`Beta`

,
`Binomial`

,
`Categorical`

,
`Cauchy`

,
`ChiSquaredNoncentral`

,
`ChiSquared`

,
`Degenerate`

,
`DiscreteUniform`

,
`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`

,
`Wald`

,
`Weibull`

,
`WeightedDiscrete`

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.