Inference techniques for Fay Herriot Model.

A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.

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.