For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above.

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 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.

Implements adaptive tau leaping to approximate the
        trajectory of a continuous-time stochastic process as
        described by Cao et al. (2007) The Journal of Chemical Physics
        <doi:10.1063/1.2745299> (aka. the Gillespie stochastic
        simulation algorithm).  This package is based upon work
        supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip
        Johnson and NIH R01-AI049334 to Rustom Antia.