<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>stan-dev.r-universe.dev</title><link>https://stan-dev.r-universe.dev</link><description>Recent package updates in stan-dev</description><generator>R-universe</generator><image><url>https://github.com/stan-dev.png</url><title>R packages by stan-dev</title><link>https://stan-dev.r-universe.dev</link></image><lastBuildDate>Sun, 06 Sep 2026 13:18:40 GMT</lastBuildDate><item><title>[stan-dev] StanHeaders 2.39.0.9000</title><author>benjamin.goodrich@columbia.edu (Ben Goodrich)</author><description>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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/34039090516</link><pubDate>Sun, 06 Sep 2026 13:18:40 GMT</pubDate><r:package>StanHeaders</r:package><r:version>2.39.0.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/rstan</r:upstream><r:article><r:source>stanmath.Rmd</r:source><r:filename>stanmath.html</r:filename><r:title>Using the Stan Math C++ Library</r:title><r:created>2018-05-24 01:10:52</r:created><r:modified>2025-01-15 12:51:50</r:modified></r:article></item><item><title>[stan-dev] rstan 2.39.0.9000</title><author>benjamin.goodrich@columbia.edu (Ben Goodrich)</author><description>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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/33692018711</link><pubDate>Wed, 02 Sep 2026 22:29:21 GMT</pubDate><r:package>rstan</r:package><r:version>2.39.0.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/rstan</r:upstream><r:article><r:source>external.Rmd</r:source><r:filename>external.html</r:filename><r:title>Interfacing with External C++ Code</r:title><r:created>2016-12-13 21:12:22</r:created><r:modified>2023-08-31 20:54:24</r:modified></r:article><r:article><r:source>rstan.Rmd</r:source><r:filename>rstan.html</r:filename><r:title>RStan: the R interface to Stan</r:title><r:created>2016-06-04 21:46:56</r:created><r:modified>2025-12-05 00:44:54</r:modified></r:article><r:article><r:source>SBC.Rmd</r:source><r:filename>SBC.html</r:filename><r:title>Simulation Based Calibration</r:title><r:created>2019-04-18 17:43:27</r:created><r:modified>2020-06-26 04:50:15</r:modified></r:article><r:article><r:source>stanfit-objects.Rmd</r:source><r:filename>stanfit-objects.html</r:filename><r:title>Accessing the contents of a stanfit object</r:title><r:created>2016-06-04 21:46:56</r:created><r:modified>2020-06-26 04:50:15</r:modified></r:article></item><item><title>[stan-dev] posterior 1.7.1</title><author>paul.buerkner@gmail.com (Paul-Christian Bürkner)</author><description>Provides useful tools for both users and developers of
Bayesian modeling software, focusing on manipulating,
summarizing, and diagnosing the output of 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)
&lt;doi:10.1214/20-BA1221&gt;.</description><link>https://github.com/r-universe/stan-dev/actions/runs/33504808532</link><pubDate>Tue, 01 Sep 2026 11:24:41 GMT</pubDate><r:package>posterior</r:package><r:version>1.7.1</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/posterior</r:upstream><r:article><r:source>pareto_diagnostics.Rmd</r:source><r:filename>pareto_diagnostics.html</r:filename><r:title>Pareto-khat diagnostics</r:title><r:created>2024-06-28 08:52:36</r:created><r:modified>2024-06-28 08:52:36</r:modified></r:article><r:article><r:source>rvar.Rmd</r:source><r:filename>rvar.html</r:filename><r:title>rvar: The Random Variable Datatype</r:title><r:created>2021-03-25 23:40:37</r:created><r:modified>2023-11-19 09:31:18</r:modified></r:article><r:article><r:source>posterior.Rmd</r:source><r:filename>posterior.html</r:filename><r:title>The posterior R package</r:title><r:created>2021-05-22 15:47:22</r:created><r:modified>2024-06-28 08:52:36</r:modified></r:article></item><item><title>[stan-dev] pkgdownconfig 2.5.0</title><author>public@visruth.com (Visruth Srimath Kandali)</author><description>This package is a template package, built off of the Stan
Quarto styling. Exclusively for use styling `pkgdown` websites
in the Stan ecosystem.</description><link>https://github.com/r-universe/stan-dev/actions/runs/33438980688</link><pubDate>Mon, 31 Aug 2026 18:56:26 GMT</pubDate><r:package>pkgdownconfig</r:package><r:version>2.5.0</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/pkgdown-config</r:upstream><r:article><r:source>Quirks.qmd</r:source><r:filename>Quirks.html</r:filename><r:title>Quirks</r:title><r:created>2025-09-22 00:03:43</r:created><r:modified>2026-07-25 07:36:34</r:modified></r:article><r:article><r:source>Setup.qmd</r:source><r:filename>Setup.html</r:filename><r:title>Setup</r:title><r:created>2025-09-21 22:33:18</r:created><r:modified>2025-12-03 19:41:58</r:modified></r:article></item><item><title>[stan-dev] rstantools 2.7.1</title><author>andrew.johnson@arjohnsonau.com (Andrew Johnson)</author><description>Provides various tools for developers of R packages
interfacing with 'Stan' &lt;https://mc-stan.org&gt;, 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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/33200949589</link><pubDate>Fri, 28 Aug 2026 14:07:32 GMT</pubDate><r:package>rstantools</r:package><r:version>2.7.1</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/rstantools</r:upstream><r:article><r:source>developer-guidelines.Rmd</r:source><r:filename>developer-guidelines.html</r:filename><r:title>Guidelines for Developers of R Packages Interfacing with Stan</r:title><r:created>2016-10-28 20:38:30</r:created><r:modified>2022-03-23 19:10:25</r:modified></r:article><r:article><r:source>minimal-rstan-package.Rmd</r:source><r:filename>minimal-rstan-package.html</r:filename><r:title>Step by step guide for creating a package that depends on RStan</r:title><r:created>2018-04-09 22:26:56</r:created><r:modified>2026-07-20 16:51:33</r:modified></r:article></item><item><title>[stan-dev] bayesplot 1.16.0.9000</title><author>jgabry@gmail.com (Jonah Gabry)</author><description>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) &lt;doi:10.1111/rssa.12378&gt;. 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'.</description><link>https://github.com/r-universe/stan-dev/actions/runs/32909252300</link><pubDate>Tue, 25 Aug 2026 22:03:49 GMT</pubDate><r:package>bayesplot</r:package><r:version>1.16.0.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/bayesplot</r:upstream><r:article><r:source>graphical-ppcs.Rmd</r:source><r:filename>graphical-ppcs.html</r:filename><r:title>Graphical posterior predictive checks using the bayesplot package</r:title><r:created>2017-08-01 19:22:12</r:created><r:modified>2026-03-26 18:14:06</r:modified></r:article><r:article><r:source>plotting-mcmc-draws.Rmd</r:source><r:filename>plotting-mcmc-draws.html</r:filename><r:title>Plotting MCMC draws using the bayesplot package</r:title><r:created>2017-08-01 19:22:12</r:created><r:modified>2026-03-26 18:14:06</r:modified></r:article><r:article><r:source>visual-mcmc-diagnostics.Rmd</r:source><r:filename>visual-mcmc-diagnostics.html</r:filename><r:title>Visual MCMC diagnostics using the bayesplot package</r:title><r:created>2017-08-01 19:22:12</r:created><r:modified>2026-04-08 21:50:18</r:modified></r:article></item><item><title>[stan-dev] loo 2.10.1.9000</title><author>jgabry@gmail.com (Jonah Gabry)</author><description>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)
&lt;doi:10.1007/s11222-016-9696-4&gt;. 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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/31380801037</link><pubDate>Mon, 10 Aug 2026 10:03:33 GMT</pubDate><r:package>loo</r:package><r:version>2.10.1.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/loo</r:upstream><r:article><r:source>loo2-lfo.Rmd</r:source><r:filename>loo2-lfo.html</r:filename><r:title>Approximate leave-future-out cross-validation for Bayesian time series models</r:title><r:created>2018-10-16 07:59:36</r:created><r:modified>2024-04-15 19:18:51</r:modified></r:article><r:article><r:source>loo2-moment-matching.Rmd</r:source><r:filename>loo2-moment-matching.html</r:filename><r:title>Avoiding model refits in leave-one-out cross-validation with moment matching</r:title><r:created>2020-03-05 18:38:35</r:created><r:modified>2025-12-22 17:00:59</r:modified></r:article><r:article><r:source>loo2-weights.Rmd</r:source><r:filename>loo2-weights.html</r:filename><r:title>Bayesian Stacking and Pseudo-BMA weights using the loo package</r:title><r:created>2018-04-04 18:15:07</r:created><r:modified>2024-04-15 19:18:51</r:modified></r:article><r:article><r:source>loo2-elpd.Rmd</r:source><r:filename>loo2-elpd.html</r:filename><r:title>Holdout validation and K-fold cross-validation of Stan programs with the loo package</r:title><r:created>2020-12-04 02:48:54</r:created><r:modified>2025-12-22 17:40:40</r:modified></r:article><r:article><r:source>loo2-non-factorized.Rmd</r:source><r:filename>loo2-non-factorized.html</r:filename><r:title>Leave-one-out cross-validation for non-factorized models</r:title><r:created>2020-06-15 14:36:32</r:created><r:modified>2024-04-15 19:18:51</r:modified></r:article><r:article><r:source>loo2-mixis.Rmd</r:source><r:filename>loo2-mixis.html</r:filename><r:title>Mixture IS leave-one-out cross-validation for high-dimensional Bayesian models</r:title><r:created>2023-03-23 19:12:13</r:created><r:modified>2024-04-15 19:18:51</r:modified></r:article><r:article><r:source>loo2-large-data.Rmd</r:source><r:filename>loo2-large-data.html</r:filename><r:title>Using Leave-one-out cross-validation for large data</r:title><r:created>2019-09-17 19:10:18</r:created><r:modified>2026-08-10 10:03:33</r:modified></r:article><r:article><r:source>loo2-example.Rmd</r:source><r:filename>loo2-example.html</r:filename><r:title>Using the loo package (version &gt;= 2.0.0)</r:title><r:created>2018-04-04 18:15:07</r:created><r:modified>2026-06-10 15:30:59</r:modified></r:article><r:article><r:source>loo2-with-rstan.Rmd</r:source><r:filename>loo2-with-rstan.html</r:filename><r:title>Writing Stan programs for use with the loo package</r:title><r:created>2018-04-04 18:15:07</r:created><r:modified>2026-08-10 10:03:33</r:modified></r:article></item><item><title>[stan-dev] posteriordb 0.3.6</title><author>mans.magnusson@gmail.com (Mans Magnusson)</author><description>R functionality of easy handling of the posteriordb
posteriors.</description><link>https://github.com/r-universe/stan-dev/actions/runs/34022334168</link><pubDate>Thu, 06 Aug 2026 09:39:16 GMT</pubDate><r:package>posteriordb</r:package><r:version>0.3.6</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/posteriordb-r</r:upstream><r:article><r:source>contributing.Rmd</r:source><r:filename>contributing.html</r:filename><r:title>Add Content to the Posterior Database using R</r:title><r:created>2022-08-12 09:15:17</r:created><r:modified>2025-04-26 19:28:54</r:modified></r:article></item><item><title>[stan-dev] projpred 2.10.0.9000</title><author>aloctavodia@gmail.com (Osvaldo Martin)</author><description>Performs projection predictive feature selection for
generalized linear models (Piironen, Paasiniemi, and Vehtari,
2020, &lt;doi:10.1214/20-EJS1711&gt;) with or without multilevel or
additive terms (Catalina, Bürkner, and Vehtari, 2022,
&lt;https://proceedings.mlr.press/v151/catalina22a.html&gt;), for
some ordinal and nominal regression models (Weber, Glass, and
Vehtari, 2025, &lt;doi:10.1007/s00180-024-01506-0&gt;), and for many
other regression models (using the latent projection by
Catalina, Bürkner, and Vehtari, 2021,
&lt;doi:10.48550/arXiv.2109.04702&gt;, 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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/33201725810</link><pubDate>Thu, 25 Jun 2026 06:42:01 GMT</pubDate><r:package>projpred</r:package><r:version>2.10.0.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/projpred</r:upstream><r:article><r:source>latent.Rmd</r:source><r:filename>latent.html</r:filename><r:title>Latent projection predictive feature selection</r:title><r:created>2023-02-06 14:02:34</r:created><r:modified>2025-12-01 08:40:41</r:modified></r:article><r:article><r:source>projpred.Rmd</r:source><r:filename>projpred.html</r:filename><r:title>projpred: Projection predictive feature selection</r:title><r:created>2021-11-18 13:49:34</r:created><r:modified>2025-12-05 16:01:53</r:modified></r:article></item><item><title>[stan-dev] shinystan 2.7.0.9000</title><author>jgabry@gmail.com (Jonah Gabry)</author><description>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).</description><link>https://github.com/r-universe/stan-dev/actions/runs/32910378541</link><pubDate>Wed, 24 Jun 2026 16:28:26 GMT</pubDate><r:package>shinystan</r:package><r:version>2.7.0.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/shinystan</r:upstream><r:article><r:source>shinystan-package.Rmd</r:source><r:filename>shinystan-package.html</r:filename><r:title>Getting Started</r:title><r:created>2015-09-17 22:41:53</r:created><r:modified>2021-03-30 21:03:39</r:modified></r:article><r:article><r:source>deploy_shinystan.Rmd</r:source><r:filename>deploy_shinystan.html</r:filename><r:title>Deploying to shinyapps.io</r:title><r:created>2015-09-17 22:41:53</r:created><r:modified>2022-03-02 19:35:49</r:modified></r:article></item><item><title>[stan-dev] cmdstanr 0.9.0</title><author>andrew.johnson@arjohnsonau.com (Andrew Johnson)</author><description>A lightweight interface to 'Stan' &lt;https://mc-stan.org&gt;.
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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/31927338953</link><pubDate>Sun, 30 Mar 2025 07:51:22 GMT</pubDate><r:package>cmdstanr</r:package><r:version>0.9.0</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/cmdstanr</r:upstream><r:article><r:source>cmdstanr.Rmd</r:source><r:filename>cmdstanr.html</r:filename><r:title>Getting started with CmdStanR</r:title><r:created>2019-10-15 20:07:10</r:created><r:modified>2024-07-31 19:46:38</r:modified></r:article><r:article><r:source>cmdstanr-internals.Rmd</r:source><r:filename>cmdstanr-internals.html</r:filename><r:title>How does CmdStanR work?</r:title><r:created>2020-06-24 06:16:55</r:created><r:modified>2023-12-13 20:14:15</r:modified></r:article><r:article><r:source>profiling.Rmd</r:source><r:filename>profiling.html</r:filename><r:title>Profiling Stan programs with CmdStanR</r:title><r:created>2021-01-25 20:53:42</r:created><r:modified>2023-07-26 20:48:49</r:modified></r:article><r:article><r:source>r-markdown.Rmd</r:source><r:filename>r-markdown.html</r:filename><r:title>R Markdown CmdStan Engine</r:title><r:created>2020-08-03 19:02:12</r:created><r:modified>2023-09-26 02:39:45</r:modified></r:article><r:article><r:source>posterior.Rmd</r:source><r:filename>posterior.html</r:filename><r:title>Working with Posteriors</r:title><r:created>2023-06-26 14:28:39</r:created><r:modified>2024-04-23 05:54:19</r:modified></r:article></item><item><title>[stan-dev] rstanarm 2.36.0.9000</title><author>benjamin.goodrich@columbia.edu (Ben Goodrich)</author><description>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.</description><link>https://github.com/r-universe/stan-dev/actions/runs/33201728395</link><pubDate>Wed, 19 Mar 2025 13:47:28 GMT</pubDate><r:package>rstanarm</r:package><r:version>2.36.0.9000</r:version><r:status>success</r:status><r:repository>https://stan-dev.r-universe.dev</r:repository><r:upstream>https://github.com/stan-dev/rstanarm</r:upstream><r:article><r:source>pooling.Rmd</r:source><r:filename>pooling.html</r:filename><r:title>Hierarchical Partial Pooling for Repeated Binary Trials</r:title><r:created>2016-02-09 04:28:31</r:created><r:modified>2023-09-12 20:48:24</r:modified></r:article><r:article><r:source>rstanarm.Rmd</r:source><r:filename>rstanarm.html</r:filename><r:title>How to Use the rstanarm Package</r:title><r:created>2015-08-29 23:30:36</r:created><r:modified>2024-04-10 22:55:59</r:modified></r:article><r:article><r:source>mrp.Rmd</r:source><r:filename>mrp.html</r:filename><r:title>MRP with rstanarm</r:title><r:created>2019-09-16 18:17:56</r:created><r:modified>2020-01-15 00:52:05</r:modified></r:article><r:article><r:source>priors.Rmd</r:source><r:filename>priors.html</r:filename><r:title>Prior Distributions for rstanarm Models</r:title><r:created>2017-04-11 08:39:45</r:created><r:modified>2022-03-16 19:55:10</r:modified></r:article><r:article><r:source>ab-testing.Rmd</r:source><r:filename>ab-testing.html</r:filename><r:title>Probabilistic A/B Testing with rstanarm</r:title><r:created>2020-10-15 23:27:44</r:created><r:modified>2022-03-16 19:55:10</r:modified></r:article><r:article><r:source>aov.Rmd</r:source><r:filename>aov.html</r:filename><r:title>Estimating ANOVA Models with rstanarm</r:title><r:created>2015-08-31 00:18:16</r:created><r:modified>2020-01-15 00:52:05</r:modified></r:article><r:article><r:source>betareg.Rmd</r:source><r:filename>betareg.html</r:filename><r:title>Modeling Rates/Proportions using Beta Regression with rstanarm</r:title><r:created>2016-12-31 18:29:15</r:created><r:modified>2020-01-15 00:52:05</r:modified></r:article><r:article><r:source>binomial.Rmd</r:source><r:filename>binomial.html</r:filename><r:title>Estimating Generalized Linear Models for Binary and Binomial Data with rstanarm</r:title><r:created>2015-09-03 18:55:03</r:created><r:modified>2020-07-08 14:13:32</r:modified></r:article><r:article><r:source>continuous.Rmd</r:source><r:filename>continuous.html</r:filename><r:title>Estimating Generalized Linear Models for Continuous Data with rstanarm</r:title><r:created>2015-12-07 17:14:22</r:created><r:modified>2024-06-05 09:24:47</r:modified></r:article><r:article><r:source>count.Rmd</r:source><r:filename>count.html</r:filename><r:title>Estimating Generalized Linear Models for Count Data with rstanarm</r:title><r:created>2015-09-04 18:10:15</r:created><r:modified>2024-06-05 09:24:47</r:modified></r:article><r:article><r:source>glmer.Rmd</r:source><r:filename>glmer.html</r:filename><r:title>Estimating Generalized (Non-)Linear Models with Group-Specific Terms with rstanarm</r:title><r:created>2016-01-08 17:14:15</r:created><r:modified>2021-05-07 23:23:17</r:modified></r:article><r:article><r:source>jm.Rmd</r:source><r:filename>jm.html</r:filename><r:title>Estimating Joint Models for Longitudinal and Time-to-Event Data with rstanarm</r:title><r:created>2017-11-11 21:47:07</r:created><r:modified>2022-03-16 19:55:10</r:modified></r:article><r:article><r:source>lm.Rmd</r:source><r:filename>lm.html</r:filename><r:title>Estimating Regularized Linear Models with rstanarm</r:title><r:created>2015-08-30 21:03:13</r:created><r:modified>2020-01-15 00:52:05</r:modified></r:article><r:article><r:source>polr.Rmd</r:source><r:filename>polr.html</r:filename><r:title>Estimating Ordinal Regression Models with rstanarm</r:title><r:created>2015-09-02 20:46:44</r:created><r:modified>2020-01-15 00:52:05</r:modified></r:article><r:article><r:source>surv.Rmd</r:source><r:filename>surv.html</r:filename><r:title>Estimating Survival (Time-to-Event) Models with rstanarm</r:title><r:created>2025-03-19 09:57:49</r:created><r:modified>2025-03-19 09:57:49</r:modified></r:article></item></channel></rss>