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paul-buerkner
brms:Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 2 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.62 score 13k scripts 35 dependents
quentingronau
bridgesampling:Bridge Sampling for Marginal Likelihoods and Bayes Factors
Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, <https://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>). Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.
Maintained by Quentin F. Gronau. Last updated 2 years ago.
32 stars 12.19 score 314 scripts 55 dependents
asael697
bayesforecast:Bayesian Time Series Modeling with Stan
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.
bayesian-inferenceforecasting-modelsmcmcstantime-series-analysiscpp
45 stars 6.92 score 62 scripts