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)