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My research group develops open-source software. Our software is publicly available on CRAN and GitHub.
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SSGL: Spike-and-slab group lasso (SSGL) for group-regularized generalized linear models (GLMs) [Paper 1, Paper 2]
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MBSP: Gaussian multivariate Bayesian linear regression with shrinkage priors (MBSP) using the three parameter beta normal family [Paper]
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NVCSSL: Nonparametric varying coefficient spike-and-slab lasso (NVC-SSL) for high-dimensional Bayesian varying coefficient models [Paper]
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neuralG: Flexible neural network-based approach for g-modeling, or mixing density estimation in a latent variable model [Paper]
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PGQR: Penalized generative quantile regression (PGQR), a deep learning generative approach for joint quantile regression [Paper]
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GBnpmle: Generative bootstrapping for nonparametric maximum likelihood estimation of a mixing density in latent variable models [Paper]
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TSM: Three-state model (TSM) framework for analysis of treatment crossover in survival trials [Paper]
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lmmprobe: LMM-PROBE, sparse high-dimensional linear mixed modeling based on a partitioned empirical Bayes ECM algorithm [Paper]
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GUD: Bayesian modal regression based on the generalized unimodal distribution (GUD) family [Paper]
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RobustBayesianCopas: Robust Bayesian Copas selection model for sensitivity analysis and quantifying the impact of publication bias [Paper]
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MtMBSP: Mixed-type multivariate Bayesian regression with shrinkage priors (Mt-MBSP) using the three parameter beta normal family [Paper]
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NormalBetaPrime: Bayesian univariate linear regression and sparse normal means estimation with the normal-beta prime prior [Paper 1, Paper 2]
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ABSORB: A Bayesian Selection model for correcting Outcome Reporting Bias (ABSORB) in multivariate meta-analysis [Paper]