rstan - R Interface to Stan
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Last updated 5 days ago
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstancpp
18.86 score 1.1k stars 281 dependents 14k scripts 69k downloads
rstanarm - Bayesian Applied Regression Modeling via Stan
Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.
Last updated 14 days ago
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp
15.70 score 393 stars 13 dependents 5.0k scripts 13k downloadsStanHeaders - C++ Header Files for Stan
The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Last updated 5 days ago
bayesian-data-analysisbayesian-inferencebayesian-statisticsmcmcstan
15.68 score 1.1k stars 346 dependents 291 scripts 70k downloadsmi - Missing Data Imputation and Model Checking
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Last updated 3 years ago
8.25 score 2 stars 47 dependents 244 scripts 26k downloadsmigui - Graphical User Interface to the 'mi' Package
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Last updated 3 years ago
1.00 score 1 scripts 297 downloads