Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.

Tool for easy and efficient discretization of continuous and categorical data. 
    The package calculates the most optimal binning of a given explanatory variable with respect to a 
    user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value 
    to each of the calculated binpoints in order to recode the original variable. 
    The package allows users to impose certain restrictions on the functional form on the resulting 
    binning while maximizing the overall information value in the original data. 
    The package is well suited for logistic scoring models where input variables may be subject to 
    restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in 
    detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring 
    is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and 
    Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum 
    information value.

Fast algorithms for fitting Bayesian variable selection
    models and computing Bayes factors, in which the outcome (or
    response variable) is modeled using a linear regression or a
    logistic regression. The algorithms are based on the variational
    approximations described in "Scalable variational inference for
    Bayesian variable selection in regression, and its accuracy in
    genetic association studies" (P. Carbonetto & M. Stephens, 2012,
    <DOI:10.1214/12-BA703>). This software has been applied to large
    data sets with over a million variables and thousands of samples.

Provides model-agnostic visual diagnostics for vector
    autoregressive (VAR) models. Given empirical data, model predictions,
    residuals, and optionally simulated data, the package assembles a
    multi-panel diagnostic grid: empirical vs. predicted time series, residual
    inspection, residuals vs. predictions scatter, and posterior predictive style
    checks via simulated trajectories. Output is a 'patchwork' object composed
    of 'ggplot2' plots, allowing further customisation via standard
    'ggplot2' theme calls. Follows the approach described in 
    Haslbeck et al. (2026) <doi:10.31234/osf.io/k6uz4_v3>.