List of distributions

Continuous distributions

• bccg(mu, sigma, nu): Box-Cox Cole and Green distribution parameterised by location mu, scale sigma, and skewness nu

• bcpe(mu, sigma, nu, tau): Box-Cox power exponential distribution parameterised by location mu, scale sigma, nu, and tau

• bct(mu, sigma, nu, tau): Box-Cox t-distribution parameterised by location mu, scale sigma, skewness nu, and degrees of freedom tau

• beta2(mu, phi): Beta distribution reparameterised by mean mu and precision phi

• exgauss(mu, sigma, lambda): Exponentially modified Gaussian distribution parameterised by location mu, scale sigma and rate lambda

• foldnorm(mu, sigma): Folded normal distribution parameterised by location mu and scale sigma

• gamma2(mean, sd): Gamma distribution reparameterised by mean and standard deviation

• gumbel(location, scale): Gumbel distribution parameterised by location and scale

• invgauss(mean, shape): Inverse Gaussian distribution parameterised by mean and shape

• laplace(mu, b): Laplace distribution parameterised by location mu and scale b

• oibeta(shape1, shape2, oneprob): One-inflated beta distribution parameterised by shape parameters shape1, shape2 and one-probability oneprob

• oibeta2(mu, phi, oneprob): One-inflated beta distribution reparameterised by mean mu, precision phi, and one-probability oneprob

• pareto(mu): Pareto distribution parameterised by mu

• powerexp(mu, sigma, nu): Power exponential distribution parameterised by mean mu, standard deviation sigma and shape nu

• powerexp2(mu, sigma, nu): Power exponential distribution reparameterised by location mu, scale sigma and shape nu

• skewnorm(xi, omega, alpha): Skew normal distribution parameterised by location xi, scale omega and skewness alpha

• skewnorm2(mean, sd, alpha): Skew normal distribution reparameterised by mean, standard deviation and skewness alpha

• skewt(mu, sigma, skew, df): Skew t-distribution parameterised by location mu, scale sigma, skewness skew and degrees of freedom df

• truncnorm(mean, sd, min, max): Truncated normal distribution parameterised by mean, standard deviation, lower bound min and upper bound max

• trunct(df, min, max): Truncated t-distribution parameterised by degrees of freedom df, lower bound min and upper bound max

• trunct2(df, mu, sigma, min, max): Truncated t-distribution parameterised location mu, scale sigma, degrees of freedom df, lower bound min and upper bound max

• t2(mu, sigma, df): Non-central and scaled t-distribution parameterised by location mu, scale sigma and degrees of freedom df

• vm(mu, kappa): Von Mises distribution parameterised by mean direction mu and concentration kappa

• wrpcauchy(mu, rho): Wrapped Cauchy distribution parameterised by mean direction mu and concentration rho

• zibeta(shape1, shape2, zeroprob): Zero-inflated beta distribution parameterised by shape parameters shape1, shape2 and zero-probability zeroprob

• zibeta2(mu, phi, zeroprob): Zero-inflated beta distribution reparameterised by mean mu, precision phi, and zero-probability zeroprob

• zigamma(shape, scale, zeroprob): Zero-inflated gamma distribution parameterised by shape and scale, with a zero-probability zeroprob

• zigamma2(mean, sd, zeroprob): Zero-inflated gamma distribution reparameterised by mean, standard deviation and zero-probability zeroprob

• ziinvgauss(mean, shape, zeroprob): Zero-inflated inverse Gaussian distribution parameterised by mean, shape and zero-probability zeroprob

• zilnorm(meanlog, sdlog, zeroprob): Zero-inflated log normal distribution parameterised by meanlog, sdlog and zero-probability zeroprob

• zoibeta(shape1, shape2, zeroprob, oneprob): Zero- and one-inflated beta distribution parameterised by shape parameters shape1, shape2, zero-probability zeroprob and one-probability oneprob

• zoibeta2(mu, phi, zeroprob, oneprob): Zero- and one-inflated beta distribution reparameterised by mean mu, precision phi, zero-probability zeroprob and one-probability oneprob

Discrete distributions

• betabinom(size, shape1, shape2): Beta-binomial distribution parameterised by size size, shape parameters shape1 and shape2

• genpois(lambda, phi): Generalised Poisson distribution parameterised by mean lambda and dispersion phi

• nbinom2(mu, size): Negative binomial distribution reparameterised by mean mu and size size

• zibinom(size, prob, zeroprob): Zero-inflated binomial distribution parameterised by size size, success probability prob and zero-probability zeroprob

• zinbinom(size, prob, zeroprob): Zero-inflated negative binomial distribution parameterised by size size, success probability prob and zero-probability zeroprob

• zinbinom2(mu, size, zeroprob): Zero-inflated negative binomial distribution reparameterised by mean mu, size size and zero-probability zeroprob

• zipois(lambda, zeroprob): Zero-inflated Poisson distribution parameterised by rate lambda and zero-probability zeroprob

• ztbinom(size, prob): Zero-truncated binomial distribution parameterised by size size and success probability prob

• ztnbinom(size, prob): Zero-truncated negative binomial distribution parameterised by size size and success probability prob

• ztnbinom2(mu, size): Zero-truncated negative binomial distribution reparameterised by mean mu and size size

• ztpois(lambda): Zero-truncated Poisson distribution parameterised by rate lambda

Multivariate distributions

• dirichlet(alpha): Dirichlet distribution parameterised by concentration parameter vector alpha

• dirmult(size, alpha): Dirichlet-multinomial distribution parameterised by size and concentration parameters alpha

• mvt(mu, Sigma, df): Multivariate t-distribution parameterised by location mu, scale matrix Sigma and degrees of freedom df

• vmf(mu, kappa): Multivariate von Mises-Fisher distribution parameterised by unit mean vector mu and concentration kappa

• vmf2(theta): Multivariate von Mises-Fisher distribution parameterised by parameter theta equal to unit mean vector mu times concentration scalar kappa

• wishart(nu, Sigma): Wishart distribution parameterised by degrees of freedom nu and scale matrix Sigma

Copulas

Bivariate copulas can be implemented in a modular way using the dcopula function together with one of the copula constructors below. Available copula constructors are:

• cgaussian(rho) (Gaussian copula)
• cclayton(theta) (Clayton copula)
• cgumbel(theta) (Gumbel copula)
• cfrank(theta) (Frank copula)