Facilitate the movement between data frames to 'xts'. Particularly
    useful when moving from 'tidyverse' to the widely used 'xts' package, which is
    the input format of choice to various other packages. It also allows the user 
    to use a 'spread_by' argument for a character column 'xts' conversion.

Boosting the likelihood of conditional and shift transformation models as introduced in <DOI:10.1007/s11222-019-09870-4>.

Implements the Threshold-Based Naive Bayes (Tb-NB) classifier
    and its iterative refinement (iTb-NB) for binary sentiment / text
    classification problems. The classifier computes a continuous
    log-likelihood ratio score per document and uses a data-driven decision
    threshold estimated via K-fold cross-validation on a user-selected
    criterion (accuracy, F1 score, Matthews correlation coefficient,
    balanced error, etc.). An optional
    iterative refinement procedure locally re-estimates the threshold in
    regions of class overlap using either Gaussian kernel density estimation
    or a Central Limit Theorem bootstrap approximation. The package exposes
    an idiomatic R formula + data.frame interface together with a
    'quanteda'-based text preprocessing pipeline, supports user-supplied
    document-feature matrices, and includes an optional word-embedding
    extension that augments the Bag-of-Words with K nearest semantic
    neighbours of each token. The package additionally implements the
    p-value extension proposed by Romano (2025) for both document- and
    feature-level interpretability via tbnb_pvalues(). Methods are
    described in Romano, Contu, Mola, Conversano (2024)
    <doi:10.1007/s11634-023-00536-8>,
    Romano, Zammarchi, Conversano (2024)
    <doi:10.1007/s10260-023-00721-1>, and Romano (2025)
    <doi:10.1007/978-3-031-96736-8_41>.

Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.