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.

Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.

Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.

Functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) 
   "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". 
   This implements and imports a  collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting 
   functions, multiple real data sets and four vignettes covering a range different applications.