Provides branding, theme application, and navigation utilities for
    applications built with 'bs4Dash' and 'shiny'. Supports configurable
    sidebar brand display modes, hover-expand behavior, and theme
    customization using CSS variables. Includes complete navbar item helpers,
    navbar structure validation, reusable brand configuration, prototype
    top-navigation support, and helpers for common navigation bar and footer
    layouts.

Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.

Contains functions to perform Bayesian inference
    using a spectral analysis of Gaussian process priors.
    Gaussian processes are represented with a Fourier series 
    based on cosine basis functions. Currently the package
    includes parametric linear models, partial linear additive
    models with/without shape restrictions, generalized linear
    additive models with/without shape restrictions, and  
    density estimation model. To maximize computational 
    efficiency, the actual Markov chain Monte Carlo sampling 
    for each model is done using codes written in FORTRAN 90.
    This software has been developed using funding supported by
    Basic Science Research Program through the National Research
    Foundation of Korea (NRF) funded by the Ministry of Education
    (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).

Provides functions to construct two-sided Bayesian simultaneous
    credible bands (BSCBs) for the regression curve in univariate polynomial
    regression over a finite covariate interval. Six methods are implemented,
    including Normal-Gamma conjugate priors (with empirical Bayes, 
    unit-information, and g-prior hyperparameter specifications), non-conjugate 
    priors fitted via Hamiltonian Monte Carlo (HMC) using 'cmdstanr', and 
    a non-informative independent Jeffreys prior approach. Also includes 
    functions for computing the empirical simultaneous coverage rate (ESCR) and 
    posterior simultaneous coverage probability (PSCP), enabling performance 
    comparison across methods. The methodology is described in: 
    Yang et al. (2026). "Bayesian simultaneous credible bands for polynomial 
    regression" <doi:10.48550/arXiv.2606.28015>.