Showing 2 of total 2 results (show query)
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 4 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.64 score 13k scripts 35 dependents
rjdverse
rjd3sts:State Space Framework and Structural Time Series with 'JDemetra+ 3.x'
R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.
Maintained by Jean Palate. Last updated 9 months ago.
2 stars 6.64 score 25 scripts 4 dependents