stan-dev r-universe repositoryhttps://stan-dev.r-universe.devPackage updated in stan-devcranlike-server https://github.com/stan-dev.png?size=400stan-dev r-universe repositoryhttps://stan-dev.r-universe.devSat, 02 Nov 2024 21:11:03 GMT[stan-dev] StanHeaders 2.35.0.9000benjamin.goodrich@columbia.edu (Ben Goodrich)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.https://github.com/r-universe/stan-dev/actions/runs/11645854649Sat, 02 Nov 2024 21:11:03 GMTStanHeaders2.35.0.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/rstanstanmath.Rmdstanmath.htmlUsing the Stan Math C++ Library2018-05-24 01:10:522023-10-16 08:13:27[stan-dev] rstan 2.35.0.9000benjamin.goodrich@columbia.edu (Ben Goodrich)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.https://github.com/r-universe/stan-dev/actions/runs/11645855597Sat, 02 Nov 2024 21:11:03 GMTrstan2.35.0.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/rstanexternal.Rmdexternal.htmlInterfacing with External C++ Code2016-12-13 21:12:222023-08-31 20:54:24rstan.Rmdrstan.htmlRStan: the R interface to Stan2016-06-04 21:46:562023-08-31 20:54:24SBC.RmdSBC.htmlSimulation Based Calibration2019-04-18 17:43:272020-06-26 04:50:15stanfit-objects.Rmdstanfit-objects.htmlAccessing the contents of a stanfit object2016-06-04 21:46:562020-06-26 04:50:15[stan-dev] loo 2.8.0.9000jsg2201@columbia.edu (Jonah Gabry)Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo, as
described in Vehtari, Gelman, and Gabry (2017)
<doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto
smoothed importance sampling (PSIS), a new procedure for
regularizing importance weights. As a byproduct of the
calculations, we also obtain approximate standard errors for
estimated predictive errors and for the comparison of
predictive errors between models. The package also provides
methods for using stacking and other model weighting techniques
to average Bayesian predictive distributions.https://github.com/r-universe/stan-dev/actions/runs/11373982260Wed, 16 Oct 2024 20:41:12 GMTloo2.8.0.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/looloo2-lfo.Rmdloo2-lfo.htmlApproximate leave-future-out cross-validation for Bayesian time series models2018-10-16 07:59:362024-04-15 19:18:51loo2-moment-matching.Rmdloo2-moment-matching.htmlAvoiding model refits in leave-one-out cross-validation with moment matching2020-03-05 18:38:352024-04-15 19:18:51loo2-weights.Rmdloo2-weights.htmlBayesian Stacking and Pseudo-BMA weights using the loo package2018-04-04 18:15:072024-04-15 19:18:51loo2-elpd.Rmdloo2-elpd.htmlHoldout validation and K-fold cross-validation of Stan programs with the loo package2020-12-04 02:48:542023-11-02 19:57:03loo2-non-factorized.Rmdloo2-non-factorized.htmlLeave-one-out cross-validation for non-factorized models2020-06-15 14:36:322024-04-15 19:18:51loo2-mixis.Rmdloo2-mixis.htmlMixture IS leave-one-out cross-validation for high-dimensional Bayesian models2023-03-23 19:12:132024-04-15 19:18:51loo2-large-data.Rmdloo2-large-data.htmlUsing Leave-one-out cross-validation for large data2019-09-17 19:10:182024-04-15 19:18:51loo2-example.Rmdloo2-example.htmlUsing the loo package (version >= 2.0.0)2018-04-04 18:15:072024-04-15 19:18:51loo2-with-rstan.Rmdloo2-with-rstan.htmlWriting Stan programs for use with the loo package2018-04-04 18:15:072024-04-15 19:18:51[stan-dev] posterior 1.6.0paul.buerkner@gmail.com (Paul-Christian Bürkner)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>.https://github.com/r-universe/stan-dev/actions/runs/11640221229Thu, 03 Oct 2024 08:56:27 GMTposterior1.6.0successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/posteriorpareto_diagnostics.Rmdpareto_diagnostics.htmlPareto-khat diagnostics2024-06-28 08:52:362024-06-28 08:52:36rvar.Rmdrvar.htmlrvar: The Random Variable Datatype2021-03-25 23:40:372023-11-19 09:31:18posterior.Rmdposterior.htmlThe posterior R package2021-05-22 15:47:222024-06-28 08:52:36[stan-dev] projpred 2.8.0.9000fweber144@protonmail.com (Frank Weber)Performs projection predictive feature selection for
generalized linear models (Piironen, Paasiniemi, and Vehtari,
2020, <doi:10.1214/20-EJS1711>) with or without multilevel or
additive terms (Catalina, Bürkner, and Vehtari, 2022,
<https://proceedings.mlr.press/v151/catalina22a.html>), for
some ordinal and nominal regression models (Weber, Glass, and
Vehtari, 2023, <arXiv:2301.01660>), and for many other
regression models (using the latent projection by Catalina,
Bürkner, and Vehtari, 2021, <arXiv:2109.04702>, which can also
be applied to most of the former models). The package is
compatible with the 'rstanarm' and 'brms' packages, but other
reference models can also be used. See the vignettes and the
documentation for more information and examples.https://github.com/r-universe/stan-dev/actions/runs/11512918796Wed, 25 Sep 2024 05:37:23 GMTprojpred2.8.0.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/projpredlatent.Rmdlatent.htmlLatent projection predictive feature selection2023-02-06 14:02:342024-06-10 20:24:20projpred.Rmdprojpred.htmlprojpred: Projection predictive feature selection2021-11-18 13:49:342024-06-10 20:24:20[stan-dev] bayesplot 1.11.1.9000jsg2201@columbia.edu (Jonah Gabry)Plotting functions for posterior analysis, MCMC
diagnostics, prior and posterior predictive checks, and other
visualizations to support the applied Bayesian workflow
advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman
(2019) <doi:10.1111/rssa.12378>. The package is designed not
only to provide convenient functionality for users, but also a
common set of functions that can be easily used by developers
working on a variety of R packages for Bayesian modeling,
particularly (but not exclusively) packages interfacing with
'Stan'.https://github.com/r-universe/stan-dev/actions/runs/11606275494Fri, 02 Aug 2024 23:06:04 GMTbayesplot1.11.1.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/bayesplotgraphical-ppcs.Rmdgraphical-ppcs.htmlGraphical posterior predictive checks using the bayesplot package2017-08-01 19:22:122021-01-07 17:06:00plotting-mcmc-draws.Rmdplotting-mcmc-draws.htmlPlotting MCMC draws using the bayesplot package2017-08-01 19:22:122022-11-15 17:49:33visual-mcmc-diagnostics.Rmdvisual-mcmc-diagnostics.htmlVisual MCMC diagnostics using the bayesplot package2017-08-01 19:22:122024-01-30 21:51:44[stan-dev] rstanarm 2.35.0.9000benjamin.goodrich@columbia.edu (Ben Goodrich)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.https://github.com/r-universe/stan-dev/actions/runs/11434306188Sun, 23 Jun 2024 19:05:09 GMTrstanarm2.35.0.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/rstanarmpooling.Rmdpooling.htmlHierarchical Partial Pooling for Repeated Binary Trials2016-02-09 04:28:312024-06-03 08:18:12rstanarm.Rmdrstanarm.htmlHow to Use the rstanarm Package2015-08-29 23:30:362023-02-07 15:23:53mrp.Rmdmrp.htmlMRP with rstanarm2019-10-02 00:24:062020-07-24 18:57:33priors.Rmdpriors.htmlPrior Distributions for rstanarm Models2017-04-11 08:39:452024-06-03 08:18:12ab-testing.Rmdab-testing.htmlProbabilistic A/B Testing with rstanarm2023-02-07 15:23:532023-02-07 15:23:53aov.Rmdaov.htmlstan_aov: Estimating ANOVA Models with rstanarm2015-08-31 00:18:162024-06-03 08:18:12betareg.Rmdbetareg.htmlstan_betareg: Modeling Rates/Proportions using Beta Regression with rstanarm2016-12-31 18:29:152024-06-03 08:18:12binomial.Rmdbinomial.htmlstan_glm: Estimating Generalized Linear Models for Binary and Binomial Data with rstanarm2015-09-03 18:55:032024-06-03 08:18:12continuous.Rmdcontinuous.htmlstan_glm: Estimating Generalized Linear Models for Continuous Data with rstanarm2015-12-07 17:14:222024-06-06 08:59:20count.Rmdcount.htmlstan_glm: Estimating Generalized Linear Models for Count Data with rstanarm2015-09-04 18:10:152024-06-06 08:59:20glmer.Rmdglmer.htmlstan_glmer: Estimating Generalized (Non-)Linear Models with Group-Specific Terms with rstanarm2016-01-08 17:14:152024-06-03 08:18:12jm.Rmdjm.htmlstan_jm: Estimating Joint Models for Longitudinal and Time-to-Event Data with rstanarm2017-11-11 21:47:072024-06-03 08:18:12lm.Rmdlm.htmlstan_lm: Estimating Regularized Linear Models with rstanarm2015-08-30 21:03:132024-06-03 08:18:12polr.Rmdpolr.htmlstan_polr: Estimating Ordinal Regression Models with rstanarm2015-09-02 20:46:442024-06-03 08:18:12surv.Rmdsurv.htmlstan_surv: Estimating Survival (Time-to-Event) Models with rstanarm2018-10-30 07:12:502024-06-03 08:18:12[stan-dev] cmdstanr 0.8.1andrew.johnson@arjohnsonau.com (Andrew Johnson)A lightweight interface to 'Stan' <https://mc-stan.org>.
The 'CmdStanR' interface is an alternative to 'RStan' that
calls the command line interface for compilation and running
algorithms instead of interfacing with C++ via 'Rcpp'. This has
many benefits including always being compatible with the latest
version of Stan, fewer installation errors, fewer unexpected
crashes in RStudio, and a more permissive license.https://github.com/r-universe/stan-dev/actions/runs/11648495121Thu, 06 Jun 2024 20:40:22 GMTcmdstanr0.8.1successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/cmdstanrcmdstanr.Rmdcmdstanr.htmlGetting started with CmdStanR2019-10-15 20:07:102024-06-03 08:28:51cmdstanr-internals.Rmdcmdstanr-internals.htmlHow does CmdStanR work?2020-06-24 06:16:552023-12-13 20:14:15profiling.Rmdprofiling.htmlProfiling Stan programs with CmdStanR2021-01-25 20:53:422023-07-26 20:48:49r-markdown.Rmdr-markdown.htmlR Markdown CmdStan Engine2020-08-03 19:02:122023-09-26 02:39:45posterior.Rmdposterior.htmlWorking with Posteriors2023-06-26 14:28:392024-04-23 05:54:19[stan-dev] rstantools 2.4.0.9000jsg2201@columbia.edu (Jonah Gabry)Provides various tools for developers of R packages
interfacing with 'Stan' <https://mc-stan.org>, including
functions to set up the required package structure, S3 generics
and default methods to unify function naming across
'Stan'-based R packages, and vignettes with recommendations for
developers.https://github.com/r-universe/stan-dev/actions/runs/11378768308Mon, 20 May 2024 19:59:53 GMTrstantools2.4.0.9000successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/rstantoolsdeveloper-guidelines.Rmddeveloper-guidelines.htmlGuidelines for Developers of R Packages Interfacing with Stan2016-10-28 20:38:302022-03-23 19:10:25minimal-rstan-package.Rmdminimal-rstan-package.htmlStep by step guide for creating a package that depends on RStan2018-04-09 22:26:562024-01-22 17:07:21[stan-dev] posteriordb 0.3.2mans.magnusson@gmail.com (Mans Magnusson)R functionality of easy handling of the posteriordb
posteriors.https://github.com/r-universe/stan-dev/actions/runs/11378768467Fri, 15 Sep 2023 17:16:19 GMTposteriordb0.3.2failurehttps://stan-dev.r-universe.devhttps://github.com/stan-dev/posteriordb-r[stan-dev] shinystan 2.6.0jsg2201@columbia.edu (Jonah Gabry)A graphical user interface for interactive Markov chain
Monte Carlo (MCMC) diagnostics and plots and tables helpful for
analyzing a posterior sample. The interface is powered by the
'Shiny' web application framework from 'RStudio' and works with
the output of MCMC programs written in any programming language
(and has extended functionality for 'Stan' models fit using the
'rstan' and 'rstanarm' packages).https://github.com/r-universe/stan-dev/actions/runs/11378768926Thu, 04 Aug 2022 14:38:58 GMTshinystan2.6.0successhttps://stan-dev.r-universe.devhttps://github.com/stan-dev/shinystanshinystan-package.Rmdshinystan-package.htmlGetting Started2015-09-17 22:41:532021-03-30 21:03:39deploy_shinystan.Rmddeploy_shinystan.htmlDeploying to shinyapps.io2015-09-17 22:41:532022-03-02 19:35:49