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
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
    Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.

Performs bivariate composite likelihood and full
        information maximum likelihood estimation for polytomous
        logit-normit (graded logistic) item response theory (IRT)
        models.

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 
    2021 <doi:10.3389/fevo.2021.588292>) can be used for 
    a variety of multivariate problems when count data are at play, including 
    principal component analysis for count data, discriminant analysis, model-based clustering and 
    network inference. Implements variational algorithms to fit such models accompanied with a set of 
    functions for visualization and diagnostic.

A system to plan analyses within the mental model
    where you have one (or more) datasets and want to run either A) the same 
    function multiple times with different arguments, or B) multiple functions. 
    This is appropriate when you have multiple strata (e.g. locations, age groups)
    that you want to apply the same function to, or you have multiple variables 
    (e.g. exposures) that you want to apply the same statistical method to, or
    when you are creating the output for a report and you need multiple different
    tables or graphs.

A simple header-only logging library for C++.
  Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include <plogr.h>' in your C++ modules to use it.

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.