pcondmvtnorm: Conditional distribution of Gaussian or Student subvectors

In lbelzile/mgp: Multivariate generalized Pareto models

Conditional distribution of Gaussian or Student subvectors

Description

The function computes the conditional distribution of the sub-components in ind given the rest and returns the distribution function of the truncated Gaussian or Student components. The location vector mu and the scale matrix sigma are those of the d+p vector. The routine relies on the CDF approximation based on minimax exponential tilting implemented in the TruncatedNormal package.

Usage

pcondmvtnorm(n = 500, ind, x, lbound = rep(-Inf, length(ind)),
  ubound = rep(Inf, length(ind)), mu, sigma, df = NULL,
  model = c("norm", "stud"), log = FALSE)

Arguments

n	sample size for simulations. Default to 500.
ind	d vector of indices with integer entries in \{1, \ldots, d+p\} for which to compute the distribution function
x	d+p vector
lbound	d vector of lower bounds for truncation
ubound	d vector of upper bounds for truncation
mu	d+p vector of location parameters
sigma	d+p by d+p scale matrix
df	degrees of freedom of the d+p dimensional Student process
model	string indicating family, either norm for Gaussian or stud for Student-t
log	logical; should log probability be returned? Default to FALSE.

Value

conditional distribution function for the components ind at x[ind]

lbelzile/mgp documentation built on Aug. 5, 2023, 2:34 a.m.