ghkvec

lower triangular Cholesky root of covariance matrix

trunpt

vector of indicators for truncation above(1) or below(0)

above

if TRUE, use Halton sequence. If FALSE, use R::runif random number generator (optional / def: TRUE)

HALTON

prime number used for Halton sequence (optional / def: the smallest prime numbers, i.e. 2, 3, 5, ...)

<code>ghkvec</code> computes the GHK approximation to the integral of a
 multivariate normal density over a half plane defined by a set
 of truncation points.

 distribution

Covers many important models used
in marketing and micro-econometrics applications.
The package includes:
Bayes Regression (univariate or multivariate dep var),
Bayes Seemingly Unrelated Regression (SUR),
Binary and Ordinal Probit,
Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit,
Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and covariates,
Hierarchical Multinomial Logits with a mixture of normals prior
and covariates,
Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
Hierarchical Negative Binomial Regression Models,
Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models,
Analysis of Multivariate Ordinal survey data with scale
usage heterogeneity (as in Rossi et al, JASA (01)),
Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric
Methods and Applications (Princeton U Press 2014).

ghkvec: Compute GHK approximation to Multivariate Normal Integrals

Description

Usage

Arguments

Value

References

Examples