Data practitioners regularly use the 'R' and 'Python' programming languages to 
    prepare data for analyses. Thus, they encode important data preprocessing decisions in 
    'R' and 'Python' code. The 'smallsets' package subsequently decodes these decisions into 
    a Smallset Timeline, a static, compact visualisation of data preprocessing decisions 
    (Lucchesi et al. (2022) <doi:10.1145/3531146.3533175>). The visualisation consists of 
    small data snapshots of different preprocessing steps. The 'smallsets' package builds this 
    visualisation from a user's dataset and preprocessing code located in an 'R', 'R Markdown', 
    'Python', or 'Jupyter Notebook' file. Users simply add structured comments with snapshot  
    instructions to the preprocessing code. One optional feature in 'smallsets' requires 
    installation of the 'Gurobi' optimisation software and 'gurobi' 'R' package, available 
    from <https://www.gurobi.com>. More information regarding the optional feature and 
    'gurobi' installation can be found in the 'smallsets' vignette.

Functions used in courses taught by Dr. Small at Drew University.

Animal movement models including Moving-Resting Process
    with Embedded Brownian Motion
    (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>;
    Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>),
    Brownian Motion with Measurement Error
    (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>),
    Moving-Resting-Handling Process with Embedded Brownian Motion
    (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>),
    Moving-Resting Process with Measurement Error
    (Hu et al., 2021, <doi:10.1111/2041-210X.13694>),
    Moving-Moving Process with two Embedded Brownian Motions.

Permits determination of a set of 
    optimal dynamic treatment regimes and sample size for a SMART design 
    in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.

SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021)  <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.