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.

Power Logit Regression for Modeling Bounded Data
c(
person("Felipe", "Queiroz", email = "ffelipeq@outlook.com", role = c("aut", "cre")),
person("Silvia", "Ferrari", email = "silviaferrari@usp.br", role = "aut"))
Fitting power logit regression models for bounded
continuous data, in which the density generator may be normal, Student-t,
power exponential, slash, hyperbolic, sinh-normal, or type II logistic.
Diagnostic tools associated with the fitted model, such as the residuals,
local influence measures, leverage measures, and goodness-of-fit statistics,
are implemented. The estimation process follows the maximum likelihood approach
and, currently, the package supports two types of estimators: the usual maximum
likelihood estimator and the penalized maximum likelihood estimator. More details
about power logit regression models are described in
Queiroz and Ferrari (2022) <arXiv:2202.01697>.
BBmisc, EnvStats, Formula, gamlss.dist, GeneralizedHyperbolic,
methods, nleqslv, stats, VGAM, zipfR
Felipe Queiroz [aut, cre],
Silvia Ferrari [aut]

Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following paper: Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.