Rstan install trouble

I was working with Stan, particularly brms and the rethinking package and was able to get it to install and run. However, I went to run some models and cannot get it to work now. I have tried uninstalling all R programs multiple times and then following the instructions for installing Stan. It keeps giving me the exact same error. See below. Any help would be appreciated.

Error in withr::set_makevars(new, path, state, assignment = assignment) :
Multiple results for CXX14FLAGS found, something is wrong.FALSE
In addition: Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found

Hi Jojo,

Sorry that this is causing you so much trouble! The problem is that your Makevars.win file has multiple entries for the CXX14FLAGS option. The current version of rstan is having some problems with the Makevars file on Windows, so it will be easiest to just rename the file and run without one until the issue is fixed.

Try running:

file.rename("~/.R/Makevars.win", "~/.R/Makevars.win.bak")

Then restart R and try your model again

Cool, thank you! I think that might have solved it. I still get the warning message below, but I think I can ignore that if I read the install instructions right.

Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found

But when I try to run a model on brms it starts to sample and then I get the following message:

Start sampling
Error in unserialize(socklist[[n]]) : error reading from connection
In addition: Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found
Error in serialize(data, node$con, xdr = FALSE) :
error writing to connection

Progress is good at least! To debug this, I’ll need a little more information.

Can you post the output from:

Sys.getenv("PATH")
Sys.getenv("BINPREF")
readLines("~/.Rprofile")
readLines("~/.Renviron")
devtools::session_info("rstan")

Sys.getenv(“BINPREF”)
[1] “”
readLines("~/.Rprofile")
Error in file(con, “r”) : cannot open the connection
In addition: Warning message:
In file(con, “r”) :
cannot open file ‘C:/Users/JoJo/Documents/.Rprofile’: No such file or directory
readLines("~/.Renviron")
[1] “PATH=”{RTOOLS40_HOME}\\usr\\bin;{PATH}""
devtools::session_info(“rstan”)

  • Session info -----------------------------------------------------------------------------------------------------------------------
    setting value
    version R version 4.0.3 (2020-10-10)
    os Windows 10 x64
    system x86_64, mingw32
    ui RStudio
    language (EN)
    collate English_United States.1252
    ctype English_United States.1252
    tz America/New_York
    date 2020-11-04

  • Packages ---------------------------------------------------------------------------------------------------------------------------
    ! package * version date lib source
    assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.3)
    backports 1.1.10 2020-09-15 [1] CRAN (R 4.0.3)
    BH 1.72.0-3 2020-01-08 [1] CRAN (R 4.0.3)
    callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.3)
    checkmate 2.0.0 2020-02-06 [1] CRAN (R 4.0.3)
    cli 2.1.0 2020-10-12 [1] CRAN (R 4.0.3)
    colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.3)
    crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.3)
    curl 4.3 2019-12-02 [1] CRAN (R 4.0.3)
    desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.3)
    digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.3)
    ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.3)
    evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.3)
    fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.3)
    farver 2.0.3 2020-01-16 [1] CRAN (R 4.0.3)
    ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.3)
    glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.3)
    gridExtra 2.3 2017-09-09 [1] CRAN (R 4.0.3)
    gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.3)
    inline 0.3.16 2020-09-06 [1] CRAN (R 4.0.3)
    isoband 0.2.2 2020-06-20 [1] CRAN (R 4.0.3)
    jsonlite 1.7.1 2020-09-07 [1] CRAN (R 4.0.3)
    labeling 0.4.2 2020-10-20 [1] CRAN (R 4.0.3)
    lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.3)
    lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.3)
    loo 2.3.1 2020-07-14 [1] CRAN (R 4.0.3)
    magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.3)
    MASS 7.3-53 2020-09-09 [2] CRAN (R 4.0.3)
    Matrix 1.2-18 2019-11-27 [2] CRAN (R 4.0.3)
    matrixStats 0.57.0 2020-09-25 [1] CRAN (R 4.0.3)
    mgcv 1.8-33 2020-08-27 [2] CRAN (R 4.0.3)
    munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.3)
    nlme 3.1-149 2020-08-23 [2] CRAN (R 4.0.3)
    pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.3)
    pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.3)
    pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.3)
    pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.3)
    praise 1.0.0 2015-08-11 [1] CRAN (R 4.0.3)
    prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.3)
    processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.3)
    ps 1.4.0 2020-10-07 [1] CRAN (R 4.0.3)
    R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.3)
    RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 4.0.3)
    Rcpp * 1.0.5 2020-07-06 [1] CRAN (R 4.0.3)
    RcppEigen 0.3.3.7.0 2019-11-16 [1] CRAN (R 4.0.3)
    D RcppParallel 5.0.2 2020-06-24 [1] CRAN (R 4.0.3)
    rlang 0.4.8 2020-10-08 [1] CRAN (R 4.0.3)
    rprojroot 1.3-2 2018-01-03 [1] CRAN (R 4.0.3)
    rstan * 2.21.2 2020-07-27 [1] CRAN (R 4.0.3)
    rstudioapi 0.11 2020-02-07 [1] CRAN (R 4.0.3)
    scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.3)
    StanHeaders * 2.21.0-6 2020-08-16 [1] CRAN (R 4.0.3)
    testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.3)
    tibble 3.0.4 2020-10-12 [1] CRAN (R 4.0.3)
    utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.3)
    V8 3.3.1 2020-10-26 [1] CRAN (R 4.0.3)
    vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.3)
    viridisLite 0.3.0 2018-02-01 [1] CRAN (R 4.0.3)
    withr 2.3.0 2020-09-22 [1] CRAN (R 4.0.3)

[1] C:/Users/JoJo/Documents/R/win-library/4.0
[2] C:/Program Files/R/R-4.0.3/library

D – DLL MD5 mismatch, broken installation.

Alright, that all looks good. Do you get the same error with the rstan example model?

Try running:

example(stan_model,run.dontrun=T)

Now I am getting:

Warning messages:
1: In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found
2: In for (i in 1:n) { :
closing unused connection 6 (<-view-localhost:11841)
3: In for (i in 1:n) { :
closing unused connection 5 (<-view-localhost:11841)
4: In for (i in 1:n) { :
closing unused connection 4 (<-view-localhost:11841)
5: In for (i in 1:n) { :
closing unused connection 3 (<-view-localhost:11841)

Those aren’t always a concern, they’re just child processes left over from running chains in parallel. Does the model sample and finish running?

So I think I did get the example to run. I could see two fit objects and the mod object. But when I try to runs a brms model I get:

Compiling Stan program…
Start sampling
Error in unserialize(socklist[[n]]) : error reading from connection
In addition: Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found
Error in serialize(data, node$con, xdr = FALSE) :
error writing to connection

Alright, so it looks like there’s a brms issue. Can you try running a brms example model:

library(brms)
brm(formula = time | cens(censored) ~ age * sex + disease 
               + (1 + age|patient), cores = 4,
               data = kidney, family = lognormal(),
               prior = c(set_prior("normal(0,5)", class = "b"),
                         set_prior("cauchy(0,2)", class = "sd"),
                         set_prior("lkj(2)", class = "cor")))

So when I run that, it also does not work. This is what I get:

Compiling Stan program…
recompiling to avoid crashing R session
Start sampling
Error in unserialize(socklist[[n]]) : error reading from connection
In addition: Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found
Error in serialize(data, node$con, xdr = FALSE) :
error writing to connection

Do you get a different output with the chains=1 and verbose=T options?

So when I try the chains =1, R just crashes. Using verbose = T, it starts to run but I get a similar error.

Compiling Stan program…
Start sampling

CHECKING DATA AND PREPROCESSING FOR MODEL ‘1e678f39ae8dd1a9aa449ac57e3cc8a9’ NOW.

COMPILING MODEL ‘1e678f39ae8dd1a9aa449ac57e3cc8a9’ NOW.

STARTING SAMPLER FOR MODEL ‘1e678f39ae8dd1a9aa449ac57e3cc8a9’ NOW.
Error in unserialize(socklist[[n]]) : error reading from connection
In addition: Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
‘C:/rtools40/usr/mingw_/bin/g++’ not found
Error in serialize(data, node$con, xdr = FALSE) :
error writing to connection

Hmm can you restart R and post the output from:

mod = "data{real y_mean;} parameters{real y;} model{y~normal(y_mean,1);}"
rstan::stan(model_code=mod,data=list(y_mean=0),chains=1,verbose=T)

First it says

TRANSLATING MODEL ‘efb77423d7adb44255913eac3d9377e8’ FROM Stan CODE TO C++ CODE NOW.
successful in parsing the Stan model ‘efb77423d7adb44255913eac3d9377e8’.
COMPILING THE C++ CODE FOR MODEL ‘efb77423d7adb44255913eac3d9377e8’ NOW.
OS: x86_64, mingw32; rstan: 2.21.2; Rcpp: 1.0.5; inline: 0.3.16

setting environment variables:
LOCAL_LIBS = “C:/Users/JoJo/Documents/R/win-library/4.0/rstan/lib/x64/libStanServices.a” -L"C:/Users/JoJo/Documents/R/win-library/4.0/StanHeaders/libs/x64" -lStanHeaders -L"C:/Users/JoJo/Documents/R/win-library/4.0/RcppParallel/lib/x64" -ltbb
PKG_CPPFLAGS = -I"C:/Users/JoJo/Documents/R/win-library/4.0/Rcpp/include/" -I"C:/Users/JoJo/Documents/R/win-library/4.0/RcppEigen/include/" -I"C:/Users/JoJo/Documents/R/win-library/4.0/RcppEigen/include/unsupported" -I"C:/Users/JoJo/Documents/R/win-library/4.0/BH/include" -I"C:/Users/JoJo/Documents/R/win-library/4.0/StanHeaders/include/src/" -I"C:/Users/JoJo/Documents/R/win-library/4.0/StanHeaders/include/" -I"C:/Users/JoJo/Documents/R/win-library/4.0/RcppParallel/include/" -I"C:/Users/JoJo/Documents/R/win-library/4.0/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include “C:/Users/JoJo/Documents/R/win-library/4.0/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp” -std=c++1y

And then:

Program source :

1 :
2 : // includes from the plugin
3 : // [[Rcpp::plugins(cpp14)]]
4 :
5 :
6 : // user includes
7 : #include <Rcpp.h>
8 : #include <rstan/io/rlist_ref_var_context.hpp>
9 : #include <rstan/io/r_ostream.hpp>
10 : #include <rstan/stan_args.hpp>
11 : #include <boost/integer/integer_log2.hpp>
12 : // Code generated by Stan version 2.21.0
13 :
14 : #include <stan/model/model_header.hpp>
15 :
16 : namespace model242412b94d4b_efb77423d7adb44255913eac3d9377e8_namespace {
17 :
18 : using std::istream;
19 : using std::string;
20 : using std::stringstream;
21 : using std::vector;
22 : using stan::io::dump;
23 : using stan::math::lgamma;
24 : using stan::model::prob_grad;
25 : using namespace stan::math;
26 :
27 : static int current_statement_begin__;
28 :
29 : stan::io::program_reader prog_reader__() {
30 : stan::io::program_reader reader;
31 : reader.add_event(0, 0, “start”, “model242412b94d4b_efb77423d7adb44255913eac3d9377e8”);
32 : reader.add_event(3, 1, “end”, “model242412b94d4b_efb77423d7adb44255913eac3d9377e8”);
33 : return reader;
34 : }
35 :
36 : class model242412b94d4b_efb77423d7adb44255913eac3d9377e8
37 : : public stan::model::model_base_crtp<model242412b94d4b_efb77423d7adb44255913eac3d9377e8> {
38 : private:
39 : double y_mean;
40 : public:
41 : model242412b94d4b_efb77423d7adb44255913eac3d9377e8(rstan::io::rlist_ref_var_context& context__,
42 : std::ostream* pstream__ = 0)
43 : : model_base_crtp(0) {
44 : ctor_body(context__, 0, pstream__);
45 : }
46 :
47 : model242412b94d4b_efb77423d7adb44255913eac3d9377e8(stan::io::var_context& context__,
48 : unsigned int random_seed__,
49 : std::ostream* pstream__ = 0)
50 : : model_base_crtp(0) {
51 : ctor_body(context__, random_seed__, pstream__);
52 : }
53 :
54 : void ctor_body(stan::io::var_context& context__,
55 : unsigned int random_seed__,
56 : std::ostream* pstream__) {
57 : typedef double local_scalar_t__;
58 :
59 : boost::ecuyer1988 base_rng__ =
60 : stan::services::util::create_rng(random_seed__, 0);
61 : (void) base_rng__; // suppress unused var warning
62 :
63 : current_statement_begin__ = -1;
64 :
65 : static const char* function__ =

66 : (void) function__; // dummy to suppress unused var warning
67 : size_t pos__;
68 : (void) pos__; // dummy to suppress unused var warning
69 : std::vector vals_i__;
70 : std::vector vals_r__;
71 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
72 : (void) DUMMY_VAR__; // suppress unused var warning
73 :
74 : try {
75 : // initialize data block variables from context__
76 : current_statement_begin__ = 1;
77 : context__.validate_dims(“data initialization”, “y_mean”, “double”, context__.to_vec());
78 : y_mean = double(0);
79 : vals_r__ = context__.vals_r(“y_mean”);
80 : pos__ = 0;
81 : y_mean = vals_r__[pos__++];
82 :
83 :
84 : // initialize transformed data variables
85 : // execute transformed data statements
86 :
87 : // validate transformed data
88 :
89 : // validate, set parameter ranges
90 : num_params_r__ = 0U;
91 : param_ranges_i__.clear();
92 : current_statement_begin__ = 1;
93 : num_params_r__ += 1;
94 : } catch (const std::exception& e) {
95 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
96 : // Next line prevents compiler griping about no return
97 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
98 : }
99 : }
100 :
101 : ~model242412b94d4b_efb77423d7adb44255913eac3d9377e8() { }

102 :
103 :
104 : void transform_inits(const stan::io::var_context& context__,
105 : std::vector& params_i__,
106 : std::vector& params_r__,
107 : std::ostream* pstream__) const {
108 : typedef double local_scalar_t__;
109 : stan::io::writer writer__(params_r__, params_i__);
110 : size_t pos__;
111 : (void) pos__; // dummy call to supress warning
112 : std::vector vals_r__;
113 : std::vector vals_i__;
114 :
115 : current_statement_begin__ = 1;
116 : if (!(context__.contains_r(“y”)))
117 : stan::lang::rethrow_located(std::runtime_error(std::string(“Variable y missing”)), current_statement_begin__, prog_reader__());
118 : vals_r__ = context__.vals_r(“y”);
119 : pos__ = 0U;
120 : context__.validate_dims(“parameter initialization”, “y”, “double”, context__.to_vec());
121 : double y(0);
122 : y = vals_r__[pos__++];
123 : try {
124 : writer__.scalar_unconstrain(y);
125 : } catch (const std::exception& e) {
126 : stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: “) + e.what()), current_statement_begin__, prog_reader__());
127 : }
128 :
129 : params_r__ = writer__.data_r();
130 : params_i__ = writer__.data_i();
131 : }
132 :
133 : void transform_inits(const stan::io::var_context& context,
134 : Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
135 : std::ostream* pstream__) const {
136 : std::vector params_r_vec;
137 : std::vector params_i_vec;
138 : transform_inits(context, params_i_vec, params_r_vec, pstream__);
139 : params_r.resize(params_r_vec.size());
140 : for (int i = 0; i < params_r.size(); ++i)
141 : params_r(i) = params_r_vec[i];
142 : }
143 :
144 :
145 : template <bool propto__, bool jacobian__, typename T__>
146 : T__ log_prob(std::vector<T__>& params_r__,
147 : std::vector& params_i__,
148 : std::ostream* pstream__ = 0) const {
149 :
150 : typedef T__ local_scalar_t__;
151 :
152 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
153 : (void) DUMMY_VAR__; // dummy to suppress unused var warning
154 :
155 : T__ lp__(0.0);
156 : stan::math::accumulator<T__> lp_accum__;
157 : try {
158 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
159 :
160 : // model parameters
161 : current_statement_begin__ = 1;
162 : local_scalar_t__ y;
163 : (void) y; // dummy to suppress unused var warning
164 : if (jacobian__)
165 : y = in__.scalar_constrain(lp__);
166 : else
167 : y = in__.scalar_constrain();
168 :
169 : // model body
170 :
171 : current_statement_begin__ = 1;
172 : lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
173 :
174 : } catch (const std::exception& e) {
175 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
176 : // Next line prevents compiler griping about no return
177 : throw std::runtime_error(”*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
178 : }
179 :
180 : lp_accum__.add(lp__);
181 : return lp_accum__.sum();
182 :
183 : } // log_prob()
184 :
185 : template <bool propto, bool jacobian, typename T_>
186 : T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
187 : std::ostream
pstream = 0) const {
188 : std::vector<T_> vec_params_r;
189 : vec_params_r.reserve(params_r.size());
190 : for (int i = 0; i < params_r.size(); ++i)
191 : vec_params_r.push_back(params_r(i));
192 : std::vector vec_params_i;
193 : return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
194 : }
195 :
196 :
197 : void get_param_names(std::vectorstd::string& names__) const {
198 : names__.resize(0);
199 : names__.push_back(“y”);
200 : }
201 :
202 :
203 : void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
204 : dimss__.resize(0);
205 : std::vector<size_t> dims__;
206 : dims__.resize(0);
207 : dimss__.push_back(dims__);
208 : }
209 :
210 : template
211 : void write_array(RNG& base_rng__,
212 : std::vector& params_r__,
213 : std::vector& params_i__,
214 : std::vector& vars__,
215 : bool include_tparams__ = true,
216 : bool include_gqs__ = true,
217 : std::ostream
pstream__ = 0) const {
218 : typedef double local_scalar_t__;
219 :
220 : vars__.resize(0);
221 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
222 : static const char
function__ = “model242412b94d4b_efb77423d7adb44255913eac3d9377e8_namespace::write_array”;

223 : (void) function__; // dummy to suppress unused var warning
224 :
225 : // read-transform, write parameters
226 : double y = in__.scalar_constrain();
227 : vars__.push_back(y);
228 :
229 : double lp__ = 0.0;
230 : (void) lp__; // dummy to suppress unused var warning
231 : stan::math::accumulator lp_accum__;
232 :
233 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
234 : (void) DUMMY_VAR__; // suppress unused var warning
235 :
236 : if (!include_tparams__ && !include_gqs__) return;
237 :
238 : try {
239 : if (!include_gqs__ && !include_tparams__) return;
240 : if (!include_gqs__) return;
241 : } catch (const std::exception& e) {
242 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
243 : // Next line prevents compiler griping about no return
244 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
245 : }
246 : }

247 :
248 : template
249 : void write_array(RNG& base_rng,
250 : Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
251 : Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
252 : bool include_tparams = true,
253 : bool include_gqs = true,
254 : std::ostream* pstream = 0) const {
255 : std::vector params_r_vec(params_r.size());
256 : for (int i = 0; i < params_r.size(); ++i)
257 : params_r_vec[i] = params_r(i);
258 : std::vector vars_vec;
259 : std::vector params_i_vec;
260 : write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
261 : vars.resize(vars_vec.size());
262 : for (int i = 0; i < vars.size(); ++i)
263 : vars(i) = vars_vec[i];
264 : }
265 :
266 : std::string model_name() const {
267 : return “model242412b94d4b_efb77423d7adb44255913eac3d9377e8”;
268 : }
269 :
270 :
271 : void constrained_param_names(std::vectorstd::string& param_names__,
272 : bool include_tparams__ = true,
273 : bool include_gqs__ = true) const {
274 : std::stringstream param_name_stream__;
275 : param_name_stream__.str(std::string());
276 : param_name_stream__ << “y”;
277 : param_names__.push_back(param_name_stream__.str());
278 :
279 : if (!include_gqs__ && !include_tparams__) return;
280 :
281 : if (include_tparams__) {
282 : }
283 :
284 : if (!include_gqs__) return;
285 : }
286 :
287 :
288 : void unconstrained_param_names(std::vectorstd::string& param_names__,
289 : bool include_tparams__ = true,
290 : bool include_gqs__ = true) const {
291 : std::stringstream param_name_stream__;
292 : param_name_stream__.str(std::string());
293 : param_name_stream__ << “y”;
294 : param_names__.push_back(param_name_stream__.str());
295 :
296 : if (!include_gqs__ && !include_tparams__) return;
297 :
298 : if (include_tparams__) {
299 : }
300 :
301 : if (!include_gqs__) return;
302 : }
303 :
304 : }; // model
305 :
306 : } // namespace
307 :
308 : typedef model242412b94d4b_efb77423d7adb44255913eac3d9377e8_namespace::model242412b94d4b_efb77423d7adb44255913eac3d9377e8 stan_model;
309 :
310 : #ifndef USING_R
311 :
312 : stan::model::model_base& new_model(
313 : stan::io::var_context& data_context,
314 : unsigned int seed,
315 : std::ostream* msg_stream) {
316 : stan_model* m = new stan_model(data_context, seed, msg_stream);
317 : return *m;
318 : }
319 :
320 : #endif
321 :
322 :
323 :
324 : #include <rstan_next/stan_fit.hpp>
325 :
326 : struct stan_model_holder {
327 : stan_model_holder(rstan::io::rlist_ref_var_context rcontext,
328 : unsigned int random_seed)
329 : : rcontext_(rcontext), random_seed_(random_seed)

fi