software
Code accompanying papers, newest first. Most repositories are maintained by the student or postdoc who led the work.
| Repository | Language | Paper | Reference |
|---|---|---|---|
| CR-MRF-ordinal-preference-data | R | Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models | Chen et al. (2026, preprint) |
| RT_SPD | Python | The Reverse Telescoping Coordinate System for Positive Definite Matrices: Geometry, Computation, and Generative Modeling | Bhadra (2026, preprint) |
| RTsampler | R | An Order of Magnitude Time Complexity Reduction for Gaussian Graphical Model Posterior Sampling Using a Reverse Telescoping Block Decomposition | Gao et al. (2026, preprint) |
| qDAGx | R | Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference | Sagar et al. (2026, preprint) |
| ExponentialGM | R | Likelihood-based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants | Chen et al. (2026, JMLR) |
| exact-approx-mcmc | R | Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model | Chen et al. (2026, AISTATS) |
| deep-alpha-kernel | R | Deep Kernel Posterior Learning under Infinite Variance Prior Weights | Loría and Bhadra (2025, ICLR) |
| multivariate_confluent_hypergeometric | R | Multivariate Confluent Hypergeometric Covariance Functions with Simultaneous Flexibility over Smoothness and Tail Decay | Yarger and Bhadra (2025, Math Geosci) |
| rBGR | R | Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks | Yao et al. (2025, Biometrics) |
| graphicalEvidence | R | Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix | Bhadra et al. (2024, JMLR) |
| alphastableNNet | R | Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance | Loría and Bhadra (2024, UAI) |
| GHS-LLA-codes | MATLAB | Maximum a posteriori estimation in graphical models using local linear approximation | Sagar et al. (2024, Stat) |
| Sure-tuned_BridgeRegression | R | SURE-tuned Bridge Regression | Loría and Bhadra (2024, Stat Comput) |
| Graphical_HSL | MATLAB | Precision matrix estimation under the horseshoe-like prior–penalty dual | Sagar et al. (2024, EJS) |
| Graphical_Evidence | MATLAB | Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix | Bhadra et al. (2024, JMLR) |
| HS-LLA-codes | R | A Laplace Mixture Representation of the Horseshoe and Some Implications | Sagar and Bhadra (2022, IEEE SPL) |
| HS_GHS | MATLAB | Joint mean–covariance estimation via the horseshoe | Li et al. (2021, JMVA) |
| GHS | MATLAB | The graphical horseshoe estimator for inverse covariance matrices | Li et al. (2019, JCGS) |
| bayes-horseshoe-plus | Stan | The horseshoe+ estimator of ultra-sparse signals | Bhadra et al. (2017, BA) |
| bayes-horseshoe-plus | Stan | Default Bayesian analysis with global-local shrinkage priors | Bhadra et al. (2016, Biometrika) |
| Code_Bhadra_Carroll_2015.zip | MATLAB | Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems | Bhadra and Carroll (2016, Stat Comput) |
| Code_Feldman_Bhadra_Kirshner_2014.zip | MATLAB | Bayesian feature selection in high-dimensional regression in presence of correlated noise | Feldman et al. (2014, Stat) |