Class: distribution.truncated.class — distribution.truncated.class • mastiff

Base class for truncated distributions

Super classes

R6.class.class -> distribution.abstract.class -> distribution.truncated.class

distribution: the untruncated distribution
t0: the lower bound for truncation. The distribution is truncated to have support [t0, t1]
t1: the upper bound for truncation. The distribution is truncated to have support [t0, t1]
support: The support of the truncated distribution, i.e. the subset of values for which the density is positives
params: Named list of distribution parameters
interfaces: The list of available class interfaces

Create a new object of class distribution.multivariate.class

distribution.truncated.class$new(
  distribution,
  t0 = distribution$support[1],
  t1 = distribution$support[2]
)

Density function for a truncated random variable.

distribution.truncated.class$d(x, log = FALSE)

Cumulative density function for a truncated random variable.

distribution.truncated.class$p(q, lower.tail = TRUE, log.p = FALSE)

q: vector of quantiles.
lower.tail: logical; if TRUE (default), probabilities are $P[ X \leq x ]$, otherwise, $P[X>x]$.
log.p: logical; if TRUE, probabilities p are given as log(p).

Quantile function for a truncated random variable.

distribution.truncated.class$q(p, lower.tail = TRUE, log.p = FALSE)

p: vector of probabilities.
lower.tail: logical; if TRUE (default), probabilities are $P[ X \leq x ]$, otherwise, $P[X>x]$.
log.p: logical; if TRUE, probabilities p are given as log(p).

Generates random deviates for a truncated random variable by inversion sampling from the truncated CDF.

distribution.truncated.class$r(n)

n: number of observations. If length( n ) > 1, the length is taken to be the number required.

The objects of this class are cloneable with this method.

distribution.truncated.class$clone(deep = FALSE)