A collection of toys to do things like generate Collatz and other interesting sequences, calculate a fraction which is a close approximation to some value (e.g., 22/7 or 355/113 for pi), and so on.

Contains four main functions (i.e., four pieces of furniture):
    table1() which produces a well-formatted table of descriptive statistics common as Table 1
    in research articles, tableC() which produces a well-formatted table of correlations,
    tableF() which provides frequency counts, and washer() which 
    is helpful in cleaning up the data. These furniture-themed functions are designed 
    to simplify common tasks in quantitative analysis. Other data summary and cleaning tools 
    are also available.

Implementations of the family of map() functions from 'purrr'
    that can be resolved using any 'future'-supported backend, e.g.
    parallel on the local machine or distributed on a compute cluster.

Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) <doi:10.48550/arXiv.2208.14959>.

Fit (generalized) linear regression models in each leaf node of a tree.
    The tree is constructed using clinical variables only. The linear regression
    models are constructed using (high-dimensional) omics variables only. The
    leaf-node-specific regression models are estimated using the penalized likelihood
    including a standard ridge (L2) penalty and a fusion penalty that links the
    leaf-node-specific regression models to one another. The intercepts of the
    leaf nodes reflect the effects of the clinical variables and are left
    unpenalized. The tree, fitted with the clinical variables only,
    should be constructed outside of the package with the 'rpart' 'R' package.
    See Goedhart and others (2024) <doi:10.48550/arXiv.2411.02396> for details
    on the method.