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  "Title": "Hypothesis Testing for Dependent Variables with Unbalanced Data",
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  "Description": "Implements hierarchical Bayesian inference, robust\nfrequentist inference, and distribution-free inference for\ndependent and unbalanced data under strong-mixing conditions.\nSupports triangular-array, weighted-sum and mixingale\nconvergence regimes with Whittle and composite likelihoods,\nheteroskedasticity-and-autocorrelation-consistent variance\nestimation, block bootstrap with automatic block length,\nfixed-bandwidth HAR inference, adaptive conformal prediction,\nBayesian decision under Region of Practical Equivalence,\nbridge-sampling Bayes factors, and predictive comparison via\nthe Widely Applicable Information Criterion and\nleave-future-out cross-validation. Methods follow Andrews\n(1991) <doi:10.2307/2938229>, Kiefer and Vogelsang (2005)\n<doi:10.1017/S0266466605050565>, Patton, Politis and White\n(2009) <doi:10.1080/07474930802459016>, Vehtari, Gelman and\nGabry (2017) <doi:10.1007/s11222-016-9696-4>, Kruschke (2018)\n<doi:10.1177/2515245918771304>, and Gibbs and Candes (2021)\n<doi:10.48550/arXiv.2106.00170>.",
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