Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to 
    Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) <arXiv:2205.14994>.

Provides a simple and trustworthy methodology for the analysis of
    misreported continuous time series using either a frequentist
    (bootstrap-based EM algorithm) or a Bayesian (MCMC via JAGS) approach.
    The frequentist method is described in Morina et al. (2021)
    <doi:10.1038/s41598-021-02620-5>. The Bayesian extension fits the same
    ARMA model with misreporting structure using a full posterior distribution,
    providing credible intervals and DIC for model comparison, as described in 
    Morina et al. (2024) <doi:10.1101/2024.02.26.24303373>.

Provides functions to calculate the minimum and maximum possible 
    values of Cronbach's alpha when item-level missing data are present. 
    Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used 
    measures of internal consistency in the social, behavioral, and medical sciences 
    (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 
    <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume 
    complete data, and listwise deletion is often applied when missingness occurs, 
    which can lead to biased or overly optimistic reliability estimates (Enders, 2003 
    <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies 
    including enumeration, Monte Carlo sampling, and optimization algorithms 
    (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares 
    Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under 
    arbitrary missing data patterns. The approach is motivated by Manski's partial 
    identification framework and pessimistic bounding ideas from optimization literature.