This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
    identification algorithm for finding ordinary differential equations for a measured 
    system from raw data (SINDy). The package includes a set of additional tools for 
    working with raw data, with an emphasis on cognitive science applications (Dale 
    and Bhat, 2018 <doi:10.1016/j.cogsys.2018.06.020>). See 
    <https://github.com/racdale/sindyr> for examples and updates.

Manage package documentation and namespaces from the command line. 
             Programmatically attach namespaces in R and Rmd script, populates 
             'Roxygen2' skeletons with information scraped from within functions and 
             populate the Imports field of the DESCRIPTION file.

When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.

Provides functions to calculate and visualise the Regional
    Consistency Probability (RCP) for single-arm multi-regional clinical
    trials (MRCTs) using the Effect Retention Approach (ERA). Six endpoint
    types are supported: continuous, binary, count (negative binomial),
    time-to-event via hazard ratio, milestone survival, and restricted mean
    survival time (RMST). For each endpoint, both a closed-form (or
    semi-analytical) solution and a Monte Carlo simulation approach are
    implemented. Two consistency evaluation methods are available: Method 1
    (effect retention in Region 1 relative to the overall population) and
    Method 2 (simultaneous positive effect across all regions). Plotting
    functions generate faceted visualisations of RCP as a function of the
    regional allocation proportion, overlaying formula and simulation results
    for direct comparison. The methodology follows the Japanese MHLW
    guidelines for MRCTs.
    Abbreviations used: RCP (Regional Consistency Probability), MRCT
    (Multi-Regional Clinical Trial), RMST (Restricted Mean Survival Time),
    MHLW (Ministry of Health, Labour and Welfare).