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>.