Provides S3 classes to represent low rank matrix
    decompositions.

A Low Rank Correction Variational Bayesian algorithm for high-dimensional multi-source 
    heterogeneous quantile linear models. More details have been written up in a paper 
    submitted to the journal Statistics in Medicine, and the details of variational
    Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>.
    It simultaneously performs parameter estimation and variable selection. The 
    algorithm supports two model settings: (1) local models, where variable selection
    is only applied to homogeneous coefficients, and (2) global models, where variable
    selection is also performed on heterogeneous coefficients. Two forms of parameter 
    estimation are output: one is the standard variational Bayesian estimation, 
    and the other is the variational Bayesian estimation corrected with low-rank adjustment.

Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.