Facilitates hierarchical clustering analysis with functions
    to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization
    techniques to the dataset, perform hierarchical clustering and
    construct scatter plot from principal component analysis to evaluate
    the groups obtained.

Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.

This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. 
    We implement an effective solution to recover the above hidden compact representation under the likelihood framework. 
    Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.

Given a database of previous treatment/placebo estimates, their standard errors and sample sizes,
      the program calculates a significance criteria and power estimate that takes into account the among
      trial variation.

It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) <doi:10.1002/pst.1756> and Jianrong Wu, Yimei Li (2020) <doi:10.1080/10543406.2019.1684305>.

A novel searching scheme for tuning parameter in high-dimensional 
             penalized regression. We propose a new estimate of the regularization
             parameter based on an estimated lower bound of the proportion of false 
             null hypotheses (Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>).
             The bound is estimated by applying the empirical null distribution of the higher 
             criticism statistic, a second-level significance testing, which is constructed
             by dependent p-values from a multi-split regression and aggregation method
             (Jeng, Zhang and Tzeng (2019) <doi:10.1080/01621459.2018.1518236>). An estimate 
             of tuning parameter in penalized regression is decided corresponding to the lower 
             bound of the proportion of false null hypotheses. Different penalized 
             regression methods are provided in the multi-split algorithm.