Hypothesis Testing for Dependent Variables with Unbalanced Data.
HTDV provides a unified R toolkit for inference on dependent,
unbalanced data under strong-mixing conditions, combining hierarchical
Bayesian estimation via Hamiltonian Monte Carlo with frequentist and
distribution-free robustness anchors (fixed-b HAR, block bootstrap,
adaptive conformal).
The framework is shipped with two pre-registered validation studies,
both reproducible end-to-end and with their summary tables exposed as
package datasets. See vignette("HTDV-validation").
-
Factorial Monte Carlo (
htdv_sim_summary). 1024-cell design crossing sample size, AR(1) coefficient, innovation tail, imbalance ratio and location shift; 500 replications per cell × 3 inferential layers; 31 hours of wall-clock on 16 cores. The Bayesian envelope holds nominal size (mean 0.0556, sd 0.013) and nominal coverage (mean 0.944) across the entire grid; HAR and bootstrap inflate to empirical size 0.60 and coverage 0.29 in the worst corners under strong persistence. The asymptotic gap that motivates the framework is visible in the data. -
External benchmarks (
htdv_empirical_benchmarks). Three public datasets compared against published references:- FRED-MD post-1984 CPI inflation against Stock and Watson (2007).
- Shiller log-CAPE against Campbell and Shiller (1998).
- US-Canada 10-year yield differential against the iid Welch baseline.
All three layers reproduce all three references with
agreementin every case. The 95% interval widths scale monotonically with the series persistence: at$\widehat\phi\approx 0.45$ Bayes is 0.81× HAR; at$\widehat\phi\approx 0.97$ it is 2.80× HAR; at near-unit-root ($\widehat\phi\approx 0.99$ ) it is 15.0× HAR. The framework's value is the visibility of this gradient.
library(HTDV)
data(htdv_sim_summary) # simulation summary, 3069 rows
data(htdv_empirical_benchmarks) # three-dataset external validation
vignette("HTDV-validation") # full narrative
remotes::install_github("IsadoreNabi/HTDV")rstan is required. Optional backends: bridgesampling (Bayes factors),
loo (WAIC / PSIS-LOO), posterior (draws utilities), bayesplot
(visualization), readxl (vignette).
| Function | Purpose |
|---|---|
htdv_fit() |
Hierarchical Bayesian HMC fit. |
htdv_envelope() |
Berger-robust envelope across models. |
htdv_lrv() |
HAC long-run variance (Andrews bandwidth). |
htdv_fixedb() |
Fixed-bandwidth HAR Wald test. |
htdv_boot() |
Block bootstrap with automatic block length. |
htdv_conformal() |
Adaptive conformal inference. |
htdv_rope() |
ROPE-based posterior decision. |
htdv_bf() |
Bridge-sampling Bayes factor. |
htdv_waic_lfo() |
WAIC and leave-future-out CV. |
htdv_stack() |
Predictive stacking. |
htdv_diagnostics() |
MCMC diagnostics. |
htdv_ppc() |
Posterior-predictive checks on dependence statistics. |
htdv_equivalence_constants() |
Explicit TAC/WSC/MPC constants. |
htdv_simstudy() |
Factorial Monte Carlo study (Section 12-bis). |
htdv_simstudy_summary() |
Aggregate per-cell results. |
htdv_simstudy_warnings() |
Flag cells in the limit-of-identification zone. |
See vignette("HTDV-intro") for a walkthrough,
vignette("HTDV-validation") for the full validation report.
Please cite both the package and the companion paper. Run
citation("HTDV") for the current BibTeX entries.
MIT.