Provides statistical process control tools for stochastic 
    textured surfaces. The current version supports the following tools:
    (1) generic modeling of stochastic textured surfaces.
    (2) local defect monitoring and diagnostics in stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018a) 
        <doi:10.1080/00401706.2017.1302362>.
    (3) global change monitoring in the nature of stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018b) 
        <doi:10.1080/00224065.2018.1507559>.
    (4) computation of dissimilarity matrix of stochastic textured 
        surface images, which was proposed by Bui and Apley (2019b)
        <doi:10.1016/j.csda.2019.01.019>.

Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.

A new reduced-rank LDA method which works for high dimensional multi-class data.

Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.

Programs to find the sample size or power of studies using the 
    Sequential Parallel Comparison Design (SPCD) and programs to analyze 
    such studies. This is a clinical trial design where 
    patients initially on placebo who did not respond are re-randomized between 
    placebo and active drug in a second phase and the results of the two phases are 
    pooled. The method of analyzing binary data with this design is described in 
    Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the 
    method of analyzing continuous data is described in Chen, Yang, 
    Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.

Fast computation of the required sample size or the
    achieved power, for GWAS studies with different types of covariate
    effects and different types of covariate-gene dependency structure.
    For the detailed description of the methodology, see Zhang (2022)
    "Power and Sample Size Computation for Genetic Association Studies
     of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.