A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.

Provides functions for the robust estimation of 
	parametric families of copulas using minimization of 
	the Maximum Mean Discrepancy, following the article
	Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022)
	<doi:10.1080/01621459.2021.2024836>.

Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.

Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
    random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing
    independent time effect; Model 3, introducing correlated time effect.
    mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.

Fit multivariate mixture of normal distribution using
        covariance structure.

Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. 
  See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.

Researchers often have expectations about the relations between means
    of different groups or standardized regression coefficients; using informative
    hypothesis testing to incorporate these expectations into the analysis through
    order constraints increases statistical power
    Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>. Another valuable
    tool, the Bayes factor, can evaluate evidence for multiple hypotheses without
    concerns about multiple testing, and can be used in Bayesian updating
    Hoijtink, Mulder, van Lissa & Gu (2019) <doi:10.1037/met0000201>. The 'bain'
    R package enables informative hypothesis testing using the Bayes factor. The
    'mmibain' package provides 'shiny' web applications based on 'bain'. The
    RepliCrisis() function launches a 'shiny' card game to simulate the evaluation
    of replication studies while the mmibain() function launches a 'shiny'
    application to fit Bayesian informative hypotheses evaluation models from
    'bain'.