Showing 6 of total 6 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 18 hours ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.62 score 13k scripts 35 dependents
stan-dev
posterior:Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
Maintained by Paul-Christian Bürkner. Last updated 2 days ago.
168 stars 16.22 score 3.3k scripts 347 dependents
poissonconsulting
mcmcr:Manipulate MCMC Samples
Functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples. For more information see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Maintained by Joe Thorley. Last updated 2 months ago.
17 stars 7.66 score 111 scripts 10 dependentspoissonconsulting
nlist:Lists of Numeric Atomic Objects
Create and manipulate numeric list ('nlist') objects. An 'nlist' is an S3 list of uniquely named numeric objects. An numeric object is an integer or double vector, matrix or array. An 'nlists' object is a S3 class list of 'nlist' objects with the same names, dimensionalities and typeofs. Numeric list objects are of interest because they are the raw data inputs for analytic engines such as 'JAGS', 'STAN' and 'TMB'. Numeric lists objects, which are useful for storing multiple realizations of of simulated data sets, can be converted to coda::mcmc and coda::mcmc.list objects.
Maintained by Joe Thorley. Last updated 3 months ago.
6 stars 7.23 score 13 scripts 12 dependentspoissonconsulting
universals:S3 Generics for Bayesian Analyses
Provides S3 generic methods and some default implementations for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples. The purpose of 'universals' is to reduce package dependencies and conflicts. The 'nlist' package implements many of the methods for its 'nlist' class.
Maintained by Joe Thorley. Last updated 3 months ago.
4 stars 6.37 score 1 scripts 20 dependentspoissonconsulting
bboutools:Boreal Caribou Survival, Recruitment and Population Growth
Estimates annual survival, recruitment and population growth for boreal caribou populations using Bayesian and Maximum Likelihood models with fixed and random effects.
Maintained by Seb Dalgarno. Last updated 2 months ago.
1 stars 5.11 score 13 scripts 2 dependents