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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)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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