Transformed.Normal.Mixture: Create a Mixture of Transformed Normal Distributions

In tfojo1/distributions:

Create a Mixture of Transformed Normal Distributions

Description

Creates a mixture of distributions representing random variables that are normal after transformation (eg, mixture of normals, log-normals, logit-normals, etc.)

Usage

Transformed.Normal.Mixture(
  means = 0,
  sds = 1,
  weights = 1,
  transformation = NULL,
  lower = -Inf,
  upper = Inf,
  var.name = NULL
)

Arguments

means, sds	The means and standard deviations of each component of the mixture (after transformation has been applied)
weights	The weights applied to each component of the mixture. Weights need not be normalized to sum to 1. If a scalar value is given, all components of the mixture will have the same weight
lower, upper	The lower and the upper bounds, if this is a bounded distribution. The bounds are on the ORIGINAL scale (before transformation). The same bounds apply for all components of the mixture

See Also

Other Canonical Mixture Distribution Constructors: Logitnormal.Mixture(), Lognormal.Mixture(), Normal.Mixture()

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(), Uniform.Distribution(), Univariate.Canonical.Distribution(), join.distributions()

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