The blocked weighted bootstrap (BBW) is an estimation technique 
    for use with data from two-stage cluster sampled surveys in which either 
    prior weighting (e.g. population-proportional sampling or PPS as used in 
    Standardized Monitoring and Assessment of Relief and Transitions or SMART 
    surveys) or posterior weighting (e.g. as used in rapid assessment method or
    RAM and simple spatial sampling method or S3M surveys) is implemented. See 
    Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of 
    bootstrap to cluster samples. See Aaron et al (2016) 
    <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) 
    <doi:10.1371/journal.pone.0162462> for application of the blocked weighted 
    bootstrap to estimate indicators from two-stage cluster sampled surveys.

Implementation of the BC3NET algorithm for gene regulatory network inference (de Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624, <doi:10.1371/journal.pone.0033624>).

Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.

Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.