Hypothesis Testing for Dependent Variables with Unbalanced Data R package HTDV version 0.2.0 Implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) <doi:10.2307/2938229>, Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009) <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>, Kruschke (2018) <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021) <doi:10.48550/arXiv.2106.00170>. Software Depends: R (>= 4.1.0) Imports: rstan (>= 2.26.0), stats, parallel, Suggests: bayesplot, bridgesampling, knitr, loo, posterior, readxl, rmarkdown, testthat (>= 3.0.0) Jose Mauricio Gomez Julian <isadorenabi@pm.me> Comprehensive R Archive Network (CRAN) Jose Mauricio Gomez Julian [aut, cre] (ORCID: <https://orcid.org/0009-0000-2412-3150>) MIT + file LICENSE (https://CRAN.R-project.org/package=HTDV/LICENSE) 2026-05-02 application/tgz https://CRAN.R-project.org/package=HTDV doi:10.32614/CRAN.package.HTDV