Showing 4 of total 4 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 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.64 score 13k scripts 35 dependents
bbolker
margins:Marginal Effects for Model Objects
An R port of the margins command from 'Stata', which can be used to calculate marginal (or partial) effects from model objects.
Maintained by Ben Bolker. Last updated 8 months ago.
2 stars 9.43 score 956 scripts 1 dependents
dmphillippo
multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data
Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.
Maintained by David M. Phillippo. Last updated 3 days ago.
35 stars 9.34 score 163 scripts
riccardo-df
ocf:Ordered Correlation Forest
Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.
Maintained by Riccardo Di Francesco. Last updated 29 days ago.
3.95 score 5 scripts 1 dependents