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HTDV  

Hypothesis Testing for Dependent Variables with Unbalanced Data
View on CRAN: Click here


Download and install HTDV package within the R console
Install from CRAN:
install.packages("HTDV")

Install from Github:
library("remotes")
install_github("cran/HTDV")

Install by package version:
library("remotes")
install_version("HTDV", "0.2.0")



Attach the package and use:
library("HTDV")
Maintained by
Jose Mauricio Gomez Julian
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-02
Latest Update: 2026-05-02
Description:
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>.
How to cite:
Jose Mauricio Gomez Julian (2026). HTDV: Hypothesis Testing for Dependent Variables with Unbalanced Data. R package version 0.2.0, https://cran.r-project.org/web/packages/HTDV. Accessed 20 Oct. 2026.
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Functions, R codes and Examples using the HTDV R package
Full HTDV package functions and examples
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