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.

Fits penalized linear mixed models that correct for
    unobserved confounding factors. 'plmmr' infers and corrects for the
    presence of unobserved confounding effects such as population
    stratification and environmental heterogeneity. It then fits a linear
    model via penalized maximum likelihood. Originally designed for the
    multivariate analysis of single nucleotide polymorphisms (SNPs)
    measured in a genome-wide association study (GWAS), 'plmmr' eliminates
    the need for subpopulation-specific analyses and post-analysis p-value
    adjustments.  Functions for the appropriate processing of 'PLINK'
    files are also supplied. For examples, see the package homepage.
    <https://pbreheny.github.io/plmmr/>.

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.