mcmcsae-TMVN-method: Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution

Description

These functions are intended for use in the method argument of create_TMVN_sampler. In future versions these functions may gain additional arguments.

Usage

m_direct()
m_Gibbs(slice = FALSE, diagnostic = FALSE, debug = FALSE)
m_HMC(
  Tsim = pi/2,
  max.events = .Machine$integer.max,
  diagnostic = FALSE,
  debug = FALSE
)
m_HMCZigZag(
  Tsim = 1,
  rate = 1,
  prec.eq = NULL,
  diagnostic = FALSE,
  max.events = .Machine$integer.max,
  adapt = FALSE,
  debug = FALSE
)
m_softTMVN(sharpness = 100, useV = FALSE, debug = FALSE)

Value

A method object, for internal use only.

Arguments

slice: if TRUE, a Gibbs within slice sampler is used.
diagnostic: whether information about violations of inequalities is printed to the screen
debug: if TRUE a breakpoint is set at the beginning of the TMVN sampling function. Mainly intended for developers.
Tsim: the duration of a Hamiltonian Monte Carlo simulated particle trajectory. This can be specified as either a single positive numeric value for a fixed simulation time, or as a function that is applied in each MCMC iteration to generates a simulation time.
max.events: maximum number of events (reflections off inequality walls and for method 'HMCZigZag' also gradient events). Default is unlimited. Specifying a finite number may speed up the sampling but may also result in a biased sampling algorithm.
rate: vector of Laplace rate parameters for method 'HMCZigZag'. It must be a positive numeric vector of length one or the number of variables.
prec.eq: positive numeric vector of length 1 or the number of equality restrictions, to control the precision with which the equality restrictions are imposed; the larger prec.eq the more precisely they will be imposed.
adapt: experimental feature: if TRUE the rate parameter will be adapted in an attempt to make the sampling algorithm more efficient.
sharpness: for method 'softTMVN', the sharpness of the soft inequalities; the larger the better the approximation of exact inequalities. It must a positive numeric vector of length one or the number of inequality restrictions.
useV: for method 'softTMVN' whether to base computations on variance instead of precision matrices.