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>.

Computes the effective range of a smoothing matrix, 
    which is a measure of the distance to which smoothing occurs. 
    This is motivated by the application of spatial splines for adjusting 
    for unmeasured spatial confounding in regression models, but the
    calculation of effective range can be applied to smoothing 
    matrices in other contexts. For algorithmic details, see Rainey and Keller
    (2024) "spconfShiny: an R Shiny application..." <doi:10.1371/journal.pone.0311440>
    and Keller and Szpiro (2020)
    "Selecting a Scale for Spatial Confounding Adjustment" <doi:10.1111/rssa.12556>.