Enable operationalized evaluation of disease outcomes in
    multiple sclerosis.  ‘MSoutcomes’ requires longitudinally recorded
    clinical data structured in long format.  The package is based on the
    research developed at Clinical Outcomes Research unit (CORe),
    University of Melbourne and Neuroimmunology Centre, Royal Melbourne
    Hospital. Kalincik et al. (2015) <doi:10.1093/brain/awv258>.
    Lorscheider et al.  (2016) <doi:10.1093/brain/aww173>. Sharmin et al.
    (2022) <doi:10.1111/ene.15406>.  Dzau et al. (2023)
    <doi:10.1136/jnnp-2023-331748>.

Given the maximum available sample size (N) for an experiment, and the target levels of Type I 
             and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT 
             the package can also obtain its operating characteristics and implement the test for a given 
             sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. 
             The proposed test has shown evidence of reducing the average sample size required to perform 
             statistical hypothesis tests at specified levels of significance and power. Currently, the package
             implements one-sample proportion tests, one and two-sample z tests, and one and two-sample 
             t tests. A brief user guidance for this package is provided below. One can also refer to the 
             supplemental information for the same.

This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, 
    which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>.
    It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). 
    For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively.
    It also provides the implementation of conventional design method, namely binomial tests for failure count data.