Showing 5 of total 5 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
nicholasjclark
mvgam:Multivariate (Dynamic) Generalized Additive Models
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Maintained by Nicholas J Clark. Last updated 10 hours ago.
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregressioncpp
148 stars 9.92 score 117 scripts
venpopov
bmm:Easy and Accessible Bayesian Measurement Models Using 'brms'
Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2023) <doi:10.31234/osf.io/umt57>.
Maintained by Vencislav Popov. Last updated 26 days ago.
15 stars 5.92 score 35 scripts
staffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 4 months ago.
5.42 score 4.4k scripts
iiasa
ibis.iSDM:Modelling framework for integrated biodiversity distribution scenarios
Integrated framework of modelling the distribution of species and ecosystems in a suitability framing. This package allows the estimation of integrated species distribution models (iSDM) based on several sources of evidence and provided presence-only and presence-absence datasets. It makes heavy use of point-process models for estimating habitat suitability and allows to include spatial latent effects and priors in the estimation. To do so 'ibis.iSDM' supports a number of engines for Bayesian and more non-parametric machine learning estimation. Further, the 'ibis.iSDM' is specifically customized to support spatial-temporal projections of habitat suitability into the future.
Maintained by Martin Jung. Last updated 5 months ago.
bayesianbiodiversityintegrated-frameworkpoisson-processscenariossdmspatial-grainspatial-predictionsspecies-distribution-modelling
21 stars 4.36 score 12 scripts 1 dependents