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)).

Computation of stopping boundaries for a single-arm trial using a
    Bayesian criterion; i.e., for each m<=n (n= total patient number of the 
    trial) the smallest number of observed toxicities is calculated
    leading to the termination of the trial/accrual according to the specified 
    criteria. The probabilities of stopping the trial/accrual at and up until 
    (resp.) the m-th patient (m<=n) is also calculated. This design is more 
    conservative than the frequentist approach (using Clopper Pearson CIs)
    which might be preferred as it concerns safety.See also Aamot et.al.(2010) 
    "Continuous monitoring of toxicity in clinical trials - simulating the risk 
    of stopping prematurely" <doi:10.5414/cpp48476>.

Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, 'runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work.

Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) <doi:10.1021/acs.est.7b01613>.