A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.

Infer the code style (which style rules are followed and which ones are 
    not) from one package and use it to check another. This makes it easier to
    find and correct the most important problems first.

Simulate clinical trials for diagnostic test devices and evaluate 
    the operating characteristics under an adaptive design with futility
    assessment determined via the posterior predictive probabilities.

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015)  <DOI:10.3390/ijms161025897>.

Implements a framework for creating boxplots where the 
    whisker lengths are determined by formal multiple testing procedures, 
    making them adaptive to sample size and data characteristics. The function bh_boxplot()
    generates boxplots that control the False Discovery 
    Rate (FDR) via the Benjamini-Hochberg procedure, and the function holm_boxplot() generates
    boxplots that control the Family-Wise Error Rate (FWER) via the Holm procedure. 
    The methods are based on the framework in Gang, Lin, and Tong (2025) 
    <doi:10.48550/arXiv.2510.20259>.

Implements adaptive gPCA, as described in: Fukuyama, J. (2017)
    <arXiv:1702.00501>. The package also includes functionality for applying
    the method to 'phyloseq' objects so that the method can be easily applied
    to microbiome data and a 'shiny' app for interactive visualization.