Density function and random numbers generation for models with support on the positive real line.
d.betap(x, mu, varphi, log = FALSE)r.betap(n, mu, varphi)
d.F(x, mu, varphi, log = FALSE)
r.F(n, mu, varphi)
d.gamma(x, mu, varphi, log = FALSE)
r.gamma(n, mu, varphi)
d.invGauss(x, mu, varphi, log = FALSE)
r.invGauss(n, mu, varphi)
d.logLogis(x, mu, varphi, log = FALSE)
r.logLogis(n, mu, varphi)
d.logNorm(x, mu, varphi, log = FALSE)
r.logNorm(n, mu, varphi)
d.chi(x, mu, log = FALSE, ...)
r.chi(n, mu, ...)
d.ray(x, mu, log = FALSE, ...)
r.ray(n, mu, ...)
vector of real values
non-negative parameter (the distribution's mean. See ‘Details’)
non-negative parameter
logical; if TRUE, probabilities \(p\) are given as \(log(p)\).
sample size
for compatibility with other functions
For any avaliable dist, ddist gives the density and rdist generates random deviates.
The length of the result is determined by n for rdist, and is the maximum of the lengths of the numerical arguments for rdist.
The numerical arguments other than n are recycled to the length of the result. Only the first elements of the logical arguments are used.
For the reparametrized Beta-Prime distribution, the functions dbetapr and rbetapr are imported from the package extraDistr. The following holds
$$shape1 = mu*varphi$$
$$shape2 = varphi + 1$$
$$scale = 1$$
For the reparametrized F distribution, the functions df and rf are imported from stats. The following holds
$$df1 = varphi$$
$$df2 = 2*mu/(mu - 1)$$
so that the parameter \(\mu\) must satisfy \(\mu > 1\).
For the reparametrized Gamma distribution, the functions dgamma and rgamma are imported from stats. The following holds
$$shape = varphi$$
$$rate = varphi/mu$$
For the reparametrized Inverse Gaussian distribution, the functions dinvGauss and rinvGauss are imported from SuppDists. The following holds
$$nu = mu$$
$$lambda = 1/varphi$$
For the reparametrized Log-logistic distribution, the functions dllogis and rllogis a are imported from actuar. The following holds
$$shape = varphi$$
$$rate = (pi/varphi)/(mu*sin(pi/varphi))$$
For the reparametrized Log-Normal distribution, the functions dlnorm and rlnorm are imported from stats. The following holds
$$meanlog = log(mu) - varphi^2/2$$
$$sdlog = varphi$$
For the reparametrized Chi-squared F distribution, the functions dchisq and rchisq are imported from stats. The following holds
$$df = mu$$
For the reparametrized Rayleigh distribution, the functions drayleigh and rrayleigh are imported from extraDistr. The following holds
$$sigma = mu/sqrt(pi/2)$$