Canonical.Mixture.Distribution: Create a mixture distribution object based off of a...

In tfojo1/distributions:

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

Usage

Canonical.Mixture.Distribution(
  name,
  dist.name,
  parameters,
  weights = 1,
  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.values = as.numeric(NA),
  variance.values = 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 vectors, where each element has parameter values that would be passed to d/r/p functions in R. For example, for a mixture of normal(0,1) and normal(1,1) distributions, parameters=list(mean=c(0,1), sd=1) or parameters=list(mean=c(0,1), sd=c(1,1))
weights	A weight for each component distribution in the mixture. The weights need not sum to 1. If a scalar is passed, all components are given the same weight
var.name	The name of the variable in this distribution
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, is.discrete	Logicals indicating whether the distribution is improper/discrete
mean.values, variance.values	If known, vectors of values for the means and variances of each component of the distribution
lower.bound, upper.bound	If this is a bounded distribution, the lower and upper bounds
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(), Logitnormal.Distribution(), Logituniform.Distribution(), Lognormal.Distribution(), Loguniform.Distribution(), Normal.Distribution(), Transformed.Normal.Distribution(), Uniform.Distribution(), Univariate.Canonical.Distribution()

Other Distribution Constructors: Autoregressive.Multivariate.Normal.Distribution(), Bernoulli.Distribution(), Beta.Distribution(), Binomial.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(), Univariate.Canonical.Distribution(), join.distributions()

tfojo1/distributions documentation built on June 1, 2025, 9:07 p.m.