Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).

R implementation of the software tools developed in the H-CUP
    (Healthcare Cost and Utilization Project) <https://hcup-us.ahrq.gov>
    and AHRQ (Agency for Healthcare Research and Quality)
    <https://www.ahrq.gov>.  It currently contains functions for mapping ICD-9
    codes to the AHRQ comorbidity measures and translating ICD-9
    (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General
    Equivalence Mappings) from CMS (Centers for Medicare and Medicaid
    Services).

Several datasets which describe the challenges and results of
    competitions in Tournament of Champions.  This data is useful for
    practicing data wrangling, graphing, and analyzing how each season of
    Tournament of Champions played out.

Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.

Find the optimal decision rules (AKA progression criteria)
    and sample size for clinical trials with three (stop/pause/go)
    outcomes. Both binary and continuous endpoints can be accommodated,
    as can cases where an adjustment is planned following a pause outcome. 
    For more details see Wilson et al. (2024) <doi:10.1186/s12874-024-02351-x>.

The best way to implement middle ware for 'shiny' Applications. 'tower'
    is designed to make implementing behavior on top of 'shiny' easy with a layering
    model for incoming HTTP requests and server sessions. 'tower' is a very minimal
    package with little overhead, it is mainly meant for other package developers
    to implement new behavior.

Non-imputational method for handling missing values in 
   a prediction context, meaning that not only are there missing
   values in the training dataset, but also some values may be missing
   in future cases to be predicted. Based on the notion of regression
   averaging (Matloff (2017, ISBN: 9781498710916)).