A set of estimators for models and (robust) covariance matrices, and tests for panel data
             econometrics, including within/fixed effects, random effects, between, first-difference, 
             nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
             panel generalized method of moments (GMM) and general FGLS models,
             mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
             with common factors, variable coefficients and limited dependent variables models.
             Test functions include model specification, serial correlation, cross-sectional dependence,
             panel unit root and panel Granger (non-)causality. Typical references are general econometrics 
             text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>),
             Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), 
             Panel Data Econometrics with R (<doi:10.1002/9781119504641>).

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/>.