SANple - Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo
Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.
Last updated 4 months ago
cppopenmp
3.48 score 3 scripts 454 downloadsbpr - Fitting Bayesian Poisson Regression
Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.
Last updated 10 months ago
openblascppopenmp
1.30 score 5 scripts 294 downloads