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
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
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
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)