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>.

We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented  covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of  factors. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2408.10542>.

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'.