Packaged: 2017-08-31 16:39:10 UTC; nickrockwood

Packaged: 2017-10-13 16:14:02 UTC; nickrockwood

Packaged: 2018-05-02 19:51:14 UTC; nickrockwood

person("Nicholas", "Rockwood", email = "nrockwood@llu.edu", role = c("aut", "cre")))
Description: Utilizes the 'lme4' and 'optimx' packages (previously the optim()
function from 'stats') to estimate (generalized) linear mixed models (GLMM)
with factor structures using a profile likelihood approach, as outlined in
Imports: lme4, Matrix (>= 1.1.1), numDeriv, stats, optimx
Packaged: 2019-08-19 15:07:45 UTC; nickrockwood

Packaged: 2020-12-13 20:55:18 UTC; nicholasrockwood

person("Nicholas", "Rockwood", email = "njrockwood@gmail.com", role = c("aut", "cre")))
Packaged: 2022-06-21 19:39:38 UTC; nicholasrockwood

Title: Multivariate Ordered Probit Model via Pairwise Likelihood
Author: Euloge Clovis Kenne Pagui [aut,cre], Antonio Canale [aut], Alan Genz [ctb],  Adelchi Azzalini [ctb]
Maintainer: Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Description: Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likehood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.

Description: Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.