Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
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Updated
May 24, 2024 - C++
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
Skill estimation systems for multiplayer competitions
Applied time series analysis in R with Stan. Allows fast Bayesian fitting of multivariate time-series models.
A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.
Bayesian Macroeconometrics C++ Library
A Preferential Bayesian optimization library for C++/Python [SIGGRAPH 2017]
Exoplanet detection in RVs with DNest4 and GPs
👩🍳 🥧 Bayesian Analysis Kit for Etiology Research via Nested Partially Latent Class Models
Bayesian Estimation of Heteroskedastic Structural Vector Autoregressions with Markov-Switching and Time-Varying Identification of the Structural Matrix
Time varying vector autoregressive state space modeling of community interactions in a Bayesian framework
Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.
C++ template library for probabilistic programming
Conditional Auto-Regressive LASSO in R
Header-only Bayes Filters Library
An R package for Bayesian semi-parametric modelling of in-vitro drug combination experiments
Dynamic Models for Survival Data
Codes for Chandra, et al. (2021+). Escaping the curse of dimensionality in Bayesian model based clustering. Please refer to the original paper for details https://arxiv.org/abs/2006.02700
Code for Scalable Bayesian Variable Selection for Negative Binomial Regression Models. See Miao et al. (2019), in Flexible Bayesian Regression Modelling, Yanan F. et al (Eds), Elsevier, to appear.
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