Computation of stopping boundaries for a single-arm trial using a 
    Bayesian criterion. 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>.