Implements a wide range of dose escalation
    designs. The focus is on model-based designs, ranging from classical and 
    modern continual reassessment methods (CRMs) based on dose-limiting toxicity
    endpoints to dual-endpoint designs taking into account a biomarker/efficacy
    outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy
    to setup a new design with custom JAGS code. However, it is also possible to
    implement 3+3 designs for comparison or models with non-Bayesian estimation. 
    The whole package is written in a modular form in the S4 class system, making it 
    very flexible for adaptation to new models, escalation or stopping rules. 
    Further details are presented in 
    Sabanes Bove et al. (2019) <doi:10.18637/jss.v089.i10>.

Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) <DOI:10.1016/j.csda.2020.106944>) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) <DOI:10.1007/s11222-018-9831-5>).

Functions to help implement the extraction / subsetting / indexing
    function '[' and replacement function '[<-' of custom matrix-like types
    (based on S3, S4, etc.), modeled as closely to the base matrix class as
    possible (with tests to prove it).

A dashboard supports the usage of 'cromwell'. 
 'Cromwell' is a scientific workflow engine for command line users.
 This package utilizes 'cromwell' REST APIs and provides these convenient 
 functions: timing diagrams for running workflows, 'cromwell' engine status, 
 a tabular workflow list. For more information about 'cromwell', 
 visit <http://cromwell.readthedocs.io>.