Software

Covariate-Dependent Product Partition Models

• Julia
  
  • ProductPartitionModels.jl implements Gaussian PPMx models (for continuous response and covariates) including imputation-free local regression on partially observed covariates. See the article for details. Code for fitting and assessing the models in R is found in VDLocalReg_examples.
• Reference
  
  • "A Projection Approach to Local Regression with Variable-Dimension Covariates," Heiner, M., Page, G. and Quintana, F. (2023+) arXiv preprint (link to paper)
    

Bayesian Nonparametric Density Autoregression

• Julia
  
  • BNP_WMReg_Joint.jl provides implementation of a nonparametric mixture of autoregressive models with lag selection. See the article for details. Code for fitting and assessing the models is found in BNP_WMAR_examples.
• Reference
  
  • Bayesian Nonparametric Density Autoregression with Lag Selection, Heiner, M. and Kottas, A. (2022), Bayesian Analysis. (link to paper)
    

Mixture Transition Distribution (MTD)

• Julia
  
  • MTD.jl provides a Bayesian implementation of the MTD model, along with our extension for model selection. See the article for details. Code for fitting and assessing the models is found in MTD_examples.
• Reference
  
  • Estimation and Selection for High-Order Markov Chains with Bayesian Mixture Transition Distribution Models, Heiner, M. and Kottas, A. (2022), Journal of Computational and Graphical Statistics. (link to paper)
    

Sparse Probability Vectors

• Julia
  
  • SparseProbVec provides functions to sample the sparse Dirichlet mixture and stick-breaking mixture distributions for probability vectors. See the article for details.
• R
  
  • SparseProbVec is an R package. See the test script and function documentation for usage examples.
• Reference
  
  • Structured priors for sparse probability vectors with application to model selection in Markov chains, Heiner, M., Kottas, A., and Munch, S. (2019), Statistics and Computing. (link to paper)