Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2026) <doi:10.1214/24-BA1470>.

Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021).

Visualization techniques, data sets, summary and inference
        procedures aimed particularly at categorical data. Special
        emphasis is given to highly extensible grid graphics. The
        package was package was originally inspired by the book 
	"Visualizing Categorical Data" by Michael Friendly and is 
	now the main support package for a new book, 
	"Discrete Data Analysis with R" by Michael Friendly and 
	David Meyer (2015).

Provides the 'vcd2df' function, which loads a IEEE 1364-1995/2001
    VCD (.vcd) file, specified as a parameter of type string containing exactly
    a file path, and returns an R dataframe containing values over time.  
    A VCD file captures the register values at discrete timepoints from a 
    simulated trace of execution of a hardware design in Verilog or VHDL.
    The returned dataframe contains a row for each register, by name, and a
    column for each time point, specified VCD-style using octothorpe-prefixed 
    multiples of the timescale as strings. 
    The only non-trivial implementation details are that (1) VCD 'x' and 'z' 
    non-numerical values are encoded as negative value -1 (as otherwise all bit
    values are positive) and (2) registers with repeated names in distinct 
    modules are ignored, rather than duplicated, as we anticipate these 
    registers to have the same values.
    Read more in arXiv preprint: 'vcd2df' -- Leveraging Data Science Insights 
    for Hardware Security Research <doi:10.48550/arXiv.2505.06470>.