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>.

There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.

Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.

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.