Download Rstan 2.26

There is an experimental branch of rstan available on github, that is up-to-date with current Stan version, but I would not install that if you are not trying to work on rstan source code or if you do not have experience with installing packages from source and dealing with any R-package related issues

If you do not need to use the CRAN version of rstan (some companies only allow installing CRAN packages), I would suggest installing rstan 2.26 as it is faster, more up-to-date and has additional bugfixes, one of the most pronounced ones is the beta_binomial_lpmf function, which was faulty for the 2.21 release.

Download Rstan 2.26

Download File 🔥 https://shoxet.com/2y5Gi3 🔥

RStan has been falling behind the current version of Stan. On CRAN now it is 2.21 and on github it is 2.26. Many seem to prefer RcmdStan, but I still prefer RStan of different reasons (such as a lot of code using Rstan, access to gradients, log_prob() and the Stan function block functions I want to unittest from R).

On Github you can access the latest Rstan version and in most cases, you can install it without compiling. The package files and binaries are available under the rstan Github Actions for the experimental branch: R-CMD-check Workflow runs stan-dev/rstan GitHub

The issue is actually with rstanarm, not rstan. When installing rstanarm, it calls stanc to generate the c++ code, but it (does not specify any additional flags]( -dev/rstanarm/blob/master/tools/make_cc.R#L22):

With today's update of RStan, I have found that on my home Windows 11 pc, the version gets updated to version 2.26.11, while on my University research desktop that runs on Windows 10 and on my Ubuntu desktop, the version gets updated to version 2.21.7. In all three cases, the Primary CRAN repository is set to "Global (CDN) - RStudio". So this isn't a repository lag issue. The only difference that I can find is that the Windows version is different. (As I noted, my home computers are running Windows 11 and Ubuntu 22.04.1 while my U research desktop is running Windows 10.)

My question, then, is there a separate version numbering system for the rstan version running on different OSs, or is there a real or meaningful difference between the 2.21.7 versions and the Windows 11 2.26.11 version?

Thanks for any explanation. Larry Hunsicker

Which version of R are you using on the different platforms?

AFAIK, the official rstan version on CRAN is 2.21.x, and that is what I have on my Xubuntu machines.

Under Windows, R changed its tool chain between R 4.1.x and 4.2.x, before 4.2.x R was distributed on Windows as 32bit and 64 bit, since 4.2.0 only as 64 bit and with another tool chain.

I had not realised the implication of that change before our semester started. To keep a long story short, I had to tell my student to (a) either use R 4.1.3, the tool chain for that version of R and install rstan from CRAN, or (b) if they wanted to use R 4.2.x to install the tool chain for that version of R and then follow the instructions that I found on some blog of the rstan community and which installed essentially rstan 2.26.x on their machine.

I only had access to Windows 10 machines, but, perhaps, there is some further difference between Windows 10 and Windows 11. If so, it would start to become scary for lecturers like me

On your last question, yes, there would be meaningful differences as Stan is evolving.

IIRC, based on reading the (latest) manual, I told my students that they can use alpha ~ standard_normal() (or was it std_normal()) in the model block to put a standard normal prior on the parameter alpha. Only to realise that that command exist in the version of Stan described in the newest manual but not in the version of Stan that is installed with rstan at the time.

There seem to be some other features that are described in the manual of Stan 2.26 (or later), that are not implemented in Stan 2.21.

Cheers, Berwin

The answer to your first question is that my R version is 4.2.1 for all three desktops, and, of course, I had to update my tool chain on all three desktops. The upgrade to R 4.2.0 caused havoc for other users of the U. research cluster, so they had to downgrade to R 4.1. But the sysop created a way for the users that wanted to upgrade to R 4.2 to do so, and I have been on 4.2.1 for a month or so.

However your question nudged me to remember another anomaly. My University desktop and my Ubuntu machine are both running RStudio version 2022.2.3, whereas my home Windows 11 machine is running RStudio version 2002.07.1. So, my Windows 11 machine is running a numerically "earlier" version than the U. remote desktop and the Ubuntu machine. I ignored this because I was not finding any problem using the earlier version of RStudio, and I mostly use my home Windows 11 machine for exploring new packages. When I check for RStudio updates, all three tell me that they are current. Again, there is something about "Global (CDN) - RStudio" that considers different versions of RStudio (and now rstan) to be "current" for different OSs.

With respect to your second post, I use rstan primarily via rstanarm and brms. (I'm not smart enough to use rstan directly. Maybe I should take your course!) All three desktops are running brms version 2.17.0 and rstanarm version 2.21.3. So, fortunately, it seems that both rstanarm and brms worked peacefully with the respective rstan versions that got updated this AM. I'll check that today with the updated versions of rstan.

But in general I think that for my production work I should be using the most updated version of software. (Except when it is a version like X.0, where I generally wait for the bugs in a new major upgrade get worked out.) So I'd like to be able to update my U. research desktop rstan to 2.26.11. Could you relocate the instructions that you "found on some blog of the rstan community and which installed essentially rstan 2.26.x on their machine"? I'd be very grateful for that. Thanks for your help. (I hope that "it [hasn't] started to become scary for lecturers like me."

RStan is the R interface to Stan. It is distributed on CRAN as the rstan package and its source code is hosted on GitHub. Before installation, make sure you have the necessary C++ toolchain for your system by following the instructions in the Getting Started documents below.

I reinstalled the most recent version of everything: R (4.2), RStudio (2022.02.2 Build 485), Rtools (4.2), rstan (2.26.x). Then, I changed the R_LIBS_USER library. I can now run rstan in RStudio. I think changing the R_LIBS_USER solved the problem.

Thanks for the quick response I actually went ahead removed that version of "rstan' and installed the version from Cran but now I have a different problem, no matter what I do whenever I try to deploy the app, it keeps timing out. It is stuck on trying install rstan.

Ahh right, you now indeed are hit with the next problem. shinyapps.io is trying to build rstan from source. To put it mildly, this is a fairly resource intensive process (Compiling C++ code always eats lots of resources).

Implementation of popular mortality models using the 'rstan' package, which provides the R interface to the 'Stan' C++ library for Bayesian estimation. The package supports well-known models proposed in the actuarial and demographic literature including the Lee-Carter (1992) and the Cairns-Blake-Dowd (2006) models. By a simple call, the user inputs deaths and exposures and the package outputs the MCMC simulations for each parameter, the log likelihoods and predictions. Moreover, the package includes tools for model selection and Bayesian model averaging by leave future-out validation.

The RStan installation, described here, assumes usage of R version 4.2.x or above. It uses the latest development version (v2.26.x) of the RStan package, because of incompatibility of the current RStan package on CRAN (v2.21.x) for R versions above v4.2.x, as mentioned on The Stan Blog: Stan & R 4.2 on Windows.

which allows to automatically save a bare version of a compiled Stan program to the hard disk so that it does not need to be recompiled (unless after changing the program). These commands will need to be run, each time the rstan library is loaded load in R.

The target audience for this book is students and researchers who want to treat statistics as an equal partner in their scientific work. We expect that the reader is willing to take the time to both understand and to run the computational analyses.

One very important characteristic that the reader should bring to this book is a can-do spirit. There will be many places where the going will get tough, and the reader will have to play around with the material, or refresh their understanding of arithmetic or middle-school algebra.The basic principles of such a can-do spirit are nicely summarized in the book by Burger and Starbird (2012); also see Levy (2021). Although we cannot summarize the insights from these books in a few words, inspired by the Burger and Starbird (2012) book, here is a short enumeration of the kind of mindset the reader will need to cultivate:

This study reports data from a larger research effort aimed at understanding atmospheric controls of rainfall in western Uganda (Diem et al. 2019a,b). Therefore, site selection for household data collection was informed by the location of rainfall zones spanning the latitudinal range of the study region, with study communities purposefully selected at the northern- and southernmost extent. We refer to these sites as Masindi and Bwindi, respectively.

To assess the association between accuracy of climate tracking and farm yield, we fit a Bayesian multilevel statistical model to farmer-level survey data and farmer climate tracking variables. The log-transformed outcome variable, crop yield, approximates a normal distribution, informing the use of a Gaussian model structure. We estimate the model using Hamiltonian Monte Carlo procedures in Stan, called through the R Statistical Environment (v.4.1.0) (R Core Team 2020) via {rstan} (v2.26.1) (Stan Development Team 2021) with the map2stan() function of the {rethinking} package (McElreath 2015). 17dc91bb1f