Univariate.Canonical.Distribution: Create a distribution object based off of a canonical,...

In tfojo1/distributions:

Create a distribution object based off of a canonical, univariate distribution

Usage

Univariate.Canonical.Distribution(
  name,
  dist.name,
  parameters,
  var.name = NULL,
  support = Continuous.Support(),
  density.function.name = paste0("d", dist.name),
  cdf.function.name = paste0("p", dist.name),
  quantile.function.name = paste0("q", dist.name),
  transformation = NULL,
  is.improper = F,
  mean.value = as.numeric(NA),
  variance.value = as.numeric(NA)
)

Arguments

name	A short, descriptive name for the distribution
dist.name	The root name of the distribution as used by d/r/p functions in R. For example, to create a normal distribution, dist.name='norm' (like dnorm, rnorm, pnorm). To create a beta distribution, dist.name='beta' (dbeta, rbeta, qbeta). This argument is only necessary if density.function, cdf.function, and quantile.function are not passed explicitly
parameters	A list of parameters that would be passed to d/r/p functions in R. For example, for a normal(0,1), parameters=list(mean=0, sd=1)
var.name	The name of the variable in this distribution
support	A support object that
transformation	A transformation object, if the named distribution operates on a transformation of the random variable (can also pass NULL for no transformation or the name of a predefined transformation - see get.defined.transformation). For example, to create a log-normal distribution, use dist.name='norm' with transformation='log
is.improper	Logicals indicating whether the distribution is improper/discrete
mean.value, variance	If known, these values will be returned for calls to get.means, get.sds, get.covariance.matrix. If passed NA, random sampling will be used to estimate distribution parameters if requested
density.function, cdf.function, quantile.function	The functions that calculate the density, cdf, and quantile respectively. These should take the standard parameters of such functions in R. density.function should take 'x', distribution-specific parameters, and 'log'. cdf.function should take 'q', distribution-specific parameters, 'lower.tail' and 'log.p'. quantile.function should take 'p', distribution-specific parameters, and 'lower.tail' and 'log.p'

See Also

get.defined.transformation, create.transformation

Other Univariate Canonical Distribution Constructors: Bernoulli.Distribution(), Beta.Distribution(), Binomial.Distribution(), Canonical.Mixture.Distribution(), Logitnormal.Distribution(), Logituniform.Distribution(), Lognormal.Distribution(), Loguniform.Distribution(), Normal.Distribution(), Transformed.Normal.Distribution(), Uniform.Distribution()

Other Distribution Constructors: Autoregressive.Multivariate.Normal.Distribution(), Bernoulli.Distribution(), Beta.Distribution(), Binomial.Distribution(), Canonical.Mixture.Distribution(), Compound.Symmetry.Multivariate.Normal.Distribution(), Constant.Distribution(), Discrete.Set.Distribution(), Empiric.Distribution(), Logitnormal.Distribution(), Logitnormal.Mixture(), Logituniform.Distribution(), Lognormal.Distribution(), Lognormal.Mixture(), Loguniform.Distribution(), Multivariate.Correlated.Uniform.Distribution(), Multivariate.Logitnormal.Distribution(), Multivariate.Lognormal.Distribution(), Multivariate.Normal.Distribution(), Normal.Distribution(), Normal.Mixture(), Smoothed.Empiric.Distribution(), Transformed.Multivariate.Normal.Distribution(), Transformed.Normal.Distribution(), Transformed.Normal.Mixture(), Uniform.Distribution(), join.distributions()

tfojo1/distributions documentation built on July 27, 2024, 3:29 p.m.