A multiple testing procedure for testing several groups of 
        hypotheses is implemented. Linear dependency among the hypotheses 
        within the same group is modeled by using hidden Markov Models. 
        It is noted that a smaller p value does not necessarily imply 
        more significance due to the dependency. A typical application is 
        to analyze genome wide association studies datasets, where SNPs 
        from the same chromosome are treated as a group and exhibit 
        strong linear genomic dependency. See Wei Z, Sun W, Wang K, 
        Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.

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>).

Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.